{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"09_PT_Multilayer_Perceptrons","provenance":[{"file_id":"https://github.com/madewithml/basics/blob/master/notebooks/09_Multilayer_Perceptrons.ipynb","timestamp":1582822364411}],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"eTdCMVl9YAXw","colab_type":"text"},"source":["#Multilayer Perceptron (MLP)\n","\n","In this lesson, we will explore multilayer perceptrons (MLPs) which are a basic type of neural network. We'll first motivate non-linear activation functions by trying to fit a linear model (logistic regression) on our non-linear spiral data. Then we'll implement an MLP using just NumPy and then with PyTorch."]},{"cell_type":"markdown","metadata":{"id":"xuabAj4PYj57","colab_type":"text"},"source":["<div align=\"left\">\n","<a href=\"https://github.com/madewithml/basics/blob/master/notebooks/09_Multilayer_Perceptrons/09_PT_Multilayer_Perceptrons.ipynb\" role=\"button\"><img class=\"notebook-badge-image\" src=\"https://img.shields.io/static/v1?label=&amp;message=View%20On%20GitHub&amp;color=586069&amp;logo=github&amp;labelColor=2f363d\"></a>&nbsp;\n","<a href=\"https://colab.research.google.com/github/madewithml/basics/blob/master/notebooks/09_Multilayer_Perceptrons/09_PT_Multilayer_Perceptrons.ipynb\"><img class=\"notebook-badge-image\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"VoMq0eFRvugb","colab_type":"text"},"source":["# Overview"]},{"cell_type":"markdown","metadata":{"id":"qWro5T5qTJJL","colab_type":"text"},"source":["Our goal is to learn a model  𝑦̂   that models  𝑦  given  𝑋 . You'll notice that neural networks are just extensions of the generalized linear methods we've seen so far but with non-linear activation functions since our data will be highly non-linear.\n","\n","<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/madewithml/images/master/basics/09_Multilayer_Perceptron/nn.png\" width=\"550\">\n","</div>\n","\n","$z_1 = XW_1$\n","\n","$a_1 = f(z_1)$\n","\n","$z_2 = a_1W_2$\n","\n","$\\hat{y} = softmax(z_2)$ # classification\n","\n","* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n","* $W_1$ = 1st layer weights | $\\in \\mathbb{R}^{DXH}$ ($H$ is the number of hidden units in layer 1)\n","* $z_1$ = outputs from first layer  $\\in \\mathbb{R}^{NXH}$\n","* $f$ = non-linear activation function\n","* $a_1$ = activation applied first layer's outputs | $\\in \\mathbb{R}^{NXH}$\n","* $W_2$ = 2nd layer weights | $\\in \\mathbb{R}^{HXC}$ ($C$ is the number of classes)\n","* $z_2$ = outputs from second layer  $\\in \\mathbb{R}^{NXH}$\n","* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NXC}$ ($N$ is the number of samples)"]},{"cell_type":"markdown","metadata":{"id":"JqxyljU18hvt","colab_type":"text"},"source":["* **Objective:**  Predict the probability of class $y$ given the inputs $X$. Non-linearity is introduced to model the complex, non-linear data.\n","* **Advantages:**\n","  * Can model non-linear patterns in the data really well.\n","* **Disadvantages:**\n","  * Overfits easily.\n","  * Computationally intensive as network increases in size.\n","  * Not easily interpretable.\n","* **Miscellaneous:** Future neural network architectures that we'll see use the MLP as a modular unit for feed forward operations (affine transformation (XW) followed by a non-linear operation)."]},{"cell_type":"markdown","metadata":{"id":"3GHB1Qi3sskB","colab_type":"text"},"source":["**Note**: We're going to leave out the bias terms $\\beta$ to avoid further crowding the backpropagation calculations."]},{"cell_type":"markdown","metadata":{"id":"9vbZa-cxlujX","colab_type":"text"},"source":["# Data"]},{"cell_type":"markdown","metadata":{"id":"XtKqNioAayCy","colab_type":"text"},"source":["## Load data"]},{"cell_type":"markdown","metadata":{"id":"X3OrtMpFayFC","colab_type":"text"},"source":["Download non-linear spiral data for a classification task."]},{"cell_type":"code","metadata":{"id":"9NfIz_4OPYpG","colab_type":"code","colab":{}},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","import urllib"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ttgClvBw9aJF","colab_type":"code","colab":{}},"source":["SEED = 1234\n","DATA_FILE = \"spiral.csv\""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hBUNRWB4DIlr","colab_type":"code","colab":{}},"source":["# Set seed for reproducibility\n","np.random.seed(SEED)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"q14QCqKxUS2v","colab_type":"code","colab":{}},"source":["# Load data from GitHub to this notebook's local drive\n","url = \"https://raw.githubusercontent.com/madewithml/basics/master/data/spiral.csv\"\n","response = urllib.request.urlopen(url)\n","html = response.read()\n","with open(DATA_FILE, 'wb') as fp:\n","    fp.write(html)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"efS3lVYETA17","colab_type":"code","outputId":"e3f0c1e5-0911-4f9b-95b0-463d3577ac59","executionInfo":{"status":"ok","timestamp":1583943346719,"user_tz":420,"elapsed":1958,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["# Load data\n","df = pd.read_csv(DATA_FILE, header=0)\n","X = df[['X1', 'X2']].values\n","y = df['color'].values\n","df.head(5)"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X1</th>\n","      <th>X2</th>\n","      <th>color</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>c1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.000457</td>\n","      <td>0.001951</td>\n","      <td>c1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.001194</td>\n","      <td>0.003826</td>\n","      <td>c1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-0.000231</td>\n","      <td>0.006008</td>\n","      <td>c1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-0.000896</td>\n","      <td>0.007966</td>\n","      <td>c1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         X1        X2 color\n","0  0.000000  0.000000    c1\n","1 -0.000457  0.001951    c1\n","2  0.001194  0.003826    c1\n","3 -0.000231  0.006008    c1\n","4 -0.000896  0.007966    c1"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"jmTwuA9kHjhA","colab_type":"code","outputId":"0e221f87-104f-47e1-e77a-7e3674f90e55","executionInfo":{"status":"ok","timestamp":1583943346720,"user_tz":420,"elapsed":1934,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["print (\"X: \", np.shape(X))\n","print (\"y: \", np.shape(y))"],"execution_count":7,"outputs":[{"output_type":"stream","text":["X:  (1500, 2)\n","y:  (1500,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jgVjStv8VnX2","colab_type":"code","outputId":"d5e64900-23b3-4762-aff3-a5c83b24cef6","executionInfo":{"status":"ok","timestamp":1583943347039,"user_tz":420,"elapsed":2221,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":281}},"source":["# Visualize data\n","plt.title(\"Generated non-linear data\")\n","colors = {'c1': 'red', 'c2': 'yellow', 'c3': 'blue'}\n","plt.scatter(X[:, 0], X[:, 1], c=[colors[_y] for _y in y], edgecolors='k', s=25)\n","plt.show()"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEICAYAAABS0fM3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"f01ldQXXS3lc","colab_type":"text"},"source":["## Split data"]},{"cell_type":"code","metadata":{"id":"38DagTg9S4ze","colab_type":"code","colab":{}},"source":["import collections\n","from sklearn.model_selection import train_test_split"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"a7HwlgP9-G52","colab_type":"code","colab":{}},"source":["TRAIN_SIZE = 0.7\n","VAL_SIZE = 0.15\n","TEST_SIZE = 0.15\n","SHUFFLE = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wRVOgVxDDcx3","colab_type":"code","colab":{}},"source":["def train_val_test_split(X, y, val_size, test_size, shuffle):\n","    \"\"\"Split data into train/val/test datasets.\n","    \"\"\"\n","    X_train, X_test, y_train, y_test = train_test_split(\n","        X, y, test_size=test_size, stratify=y, shuffle=shuffle)\n","    X_train, X_val, y_train, y_val = train_test_split(\n","        X_train, y_train, test_size=val_size, stratify=y_train, shuffle=shuffle)\n","    return X_train, X_val, X_test, y_train, y_val, y_test"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gGFqcqTDXhkl","colab_type":"code","outputId":"7c5bb22e-ea4b-4ffe-934d-c315edc935b2","executionInfo":{"status":"ok","timestamp":1583943351849,"user_tz":420,"elapsed":749,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X=X, y=y, val_size=VAL_SIZE, test_size=TEST_SIZE, shuffle=SHUFFLE)\n","class_counts = dict(collections.Counter(y))\n","print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n","print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n","print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n","print (f\"Sample point: {X_train[0]} → {y_train[0]}\")\n","print (f\"Classes: {class_counts}\")"],"execution_count":12,"outputs":[{"output_type":"stream","text":["X_train: (1083, 2), y_train: (1083,)\n","X_val: (192, 2), y_val: (192,)\n","X_test: (225, 2), y_test: (225,)\n","Sample point: [ 0.23623443 -0.59618506] → c1\n","Classes: {'c1': 500, 'c3': 500, 'c2': 500}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"w8EQEwO2OWfN","colab_type":"text"},"source":["## Label encoder"]},{"cell_type":"code","metadata":{"id":"0ldFOhAqObcd","colab_type":"code","colab":{}},"source":["from sklearn.preprocessing import LabelEncoder"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"mYjAYlTdObf0","colab_type":"code","colab":{}},"source":["# Output vectorizer\n","y_tokenizer = LabelEncoder()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VO1uxrdgObkf","colab_type":"code","outputId":"4abbfa28-ae1b-48f3-b746-867a7ead867d","executionInfo":{"status":"ok","timestamp":1583943354684,"user_tz":420,"elapsed":571,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Fit on train data\n","y_tokenizer = y_tokenizer.fit(y_train)\n","classes = list(y_tokenizer.classes_)\n","print (f\"classes: {classes}\")"],"execution_count":15,"outputs":[{"output_type":"stream","text":["classes: ['c1', 'c2', 'c3']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1t9vjih9Oeq9","colab_type":"code","outputId":"21a391ec-dc3f-4cc5-a43d-b58bd4327f33","executionInfo":{"status":"ok","timestamp":1583943356234,"user_tz":420,"elapsed":1122,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Convert labels to tokens\n","print (f\"y_train[0]: {y_train[0]}\")\n","y_train = y_tokenizer.transform(y_train)\n","y_val = y_tokenizer.transform(y_val)\n","y_test = y_tokenizer.transform(y_test)\n","print (f\"y_train[0]: {y_train[0]}\")"],"execution_count":16,"outputs":[{"output_type":"stream","text":["y_train[0]: c1\n","y_train[0]: 0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QVgNHgSkInPE","colab_type":"code","outputId":"b69b879b-69dd-470c-f2d9-c64275b7f0dc","executionInfo":{"status":"ok","timestamp":1583943356799,"user_tz":420,"elapsed":981,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Class weights\n","counts = collections.Counter(y_train)\n","class_weights = {_class: 1.0/count for _class, count in counts.items()}\n","print (f\"class counts: {counts},\\nclass weights: {class_weights}\")"],"execution_count":17,"outputs":[{"output_type":"stream","text":["class counts: Counter({0: 361, 2: 361, 1: 361}),\n","class weights: {0: 0.002770083102493075, 2: 0.002770083102493075, 1: 0.002770083102493075}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"MDFM5gQte5rQ","colab_type":"text"},"source":["## Standardize data"]},{"cell_type":"markdown","metadata":{"id":"JlmiwCUre_ZH","colab_type":"text"},"source":["We need to standardize our data (zero mean and unit variance) in order to optimize quickly. We're only going to standardize the inputs X because out outputs y are class values."]},{"cell_type":"code","metadata":{"id":"D4uCG3vMe52J","colab_type":"code","colab":{}},"source":["from sklearn.preprocessing import StandardScaler"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3tTbwUOme5z7","colab_type":"code","colab":{}},"source":["# Standardize the data (mean=0, std=1) using training data\n","X_scaler = StandardScaler().fit(X_train)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zGTQNaRie5xb","colab_type":"code","colab":{}},"source":["# Apply scaler on training and test data (don't standardize outputs for classification)\n","X_train = X_scaler.transform(X_train)\n","X_val = X_scaler.transform(X_val)\n","X_test = X_scaler.transform(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dstH0Cm-fLgK","colab_type":"code","outputId":"9c6215c8-effd-4452-c613-5b2f1d26d801","executionInfo":{"status":"ok","timestamp":1583943360179,"user_tz":420,"elapsed":2042,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":119}},"source":["# Check (means should be ~0 and std should be ~1)\n","print (f\"X_train[0]: mean: {np.mean(X_train[:, 0], axis=0):.1f}, std: {np.std(X_train[:, 0], axis=0):.1f}\")\n","print (f\"X_train[1]: mean: {np.mean(X_train[:, 1], axis=0):.1f}, std: {np.std(X_train[:, 1], axis=0):.1f}\")\n","print (f\"X_val[0]: mean: {np.mean(X_val[:, 0], axis=0):.1f}, std: {np.std(X_val[:, 0], axis=0):.1f}\")\n","print (f\"X_val[1]: mean: {np.mean(X_val[:, 1], axis=0):.1f}, std: {np.std(X_val[:, 1], axis=0):.1f}\")\n","print (f\"X_test[0]: mean: {np.mean(X_test[:, 0], axis=0):.1f}, std: {np.std(X_test[:, 0], axis=0):.1f}\")\n","print (f\"X_test[1]: mean: {np.mean(X_test[:, 1], axis=0):.1f}, std: {np.std(X_test[:, 1], axis=0):.1f}\")"],"execution_count":21,"outputs":[{"output_type":"stream","text":["X_train[0]: mean: 0.0, std: 1.0\n","X_train[1]: mean: 0.0, std: 1.0\n","X_val[0]: mean: 0.1, std: 1.0\n","X_val[1]: mean: 0.0, std: 1.0\n","X_test[0]: mean: 0.1, std: 1.0\n","X_test[1]: mean: -0.1, std: 0.9\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IHofozO7RIiV","colab_type":"text"},"source":["# Linear model"]},{"cell_type":"markdown","metadata":{"id":"DlVmr5XkRMCf","colab_type":"text"},"source":["Before we get to our neural network, we're going to motivate non-linear activation functions by implementing a generalized linear model (logistic regression). We'll see why linear models (with linear activations) won't suffice for our dataset."]},{"cell_type":"code","metadata":{"id":"4K2qHbffAeGL","colab_type":"code","colab":{}},"source":["import torch"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"bn9Kr2XrAeCR","colab_type":"code","outputId":"21d0523d-3e32-41f7-a714-524bdf4dd08b","executionInfo":{"status":"ok","timestamp":1583943361948,"user_tz":420,"elapsed":2443,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Set seed for reproducibility\n","torch.manual_seed(SEED)"],"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<torch._C.Generator at 0x7f057f308d50>"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"markdown","metadata":{"id":"5YkMN-nU-gz7","colab_type":"text"},"source":["## Model"]},{"cell_type":"code","metadata":{"id":"hMzXPa6g-467","colab_type":"code","colab":{}},"source":["from torch import nn\n","import torch.nn.functional as F\n","from torchsummary import summary"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"RJlz9hEe_2xX","colab_type":"code","colab":{}},"source":["INPUT_DIM = X_train.shape[1] # X is 2-dimensional\n","HIDDEN_DIM = 100\n","NUM_CLASSES = len(classes) # 3 classes"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"oolBQZaE-43y","colab_type":"code","colab":{}},"source":["class LinearModel(nn.Module):\n","    def __init__(self, input_dim, hidden_dim, num_classes):\n","        super(LinearModel, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, hidden_dim)\n","        self.fc2 = nn.Linear(hidden_dim, num_classes)\n","        \n","    def forward(self, x_in, apply_softmax=False):\n","        z = self.fc1(x_in) # linear activation\n","        y_pred = self.fc2(z)\n","        if apply_softmax:\n","            y_pred = F.softmax(y_pred, dim=1) \n","        return y_pred"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"q1aORSTK-41F","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":340},"outputId":"e0eddab6-b3cb-434c-ae31-d029c4451172","executionInfo":{"status":"ok","timestamp":1583943383373,"user_tz":420,"elapsed":325,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}}},"source":["# Initialize model\n","model = LinearModel(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, num_classes=NUM_CLASSES)\n","print (model.named_parameters)\n","summary(model, input_size=(INPUT_DIM,))"],"execution_count":30,"outputs":[{"output_type":"stream","text":["<bound method Module.named_parameters of LinearModel(\n","  (fc1): Linear(in_features=2, out_features=100, bias=True)\n","  (fc2): Linear(in_features=100, out_features=3, bias=True)\n",")>\n","----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Linear-1                  [-1, 100]             300\n","            Linear-2                    [-1, 3]             303\n","================================================================\n","Total params: 603\n","Trainable params: 603\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.00\n","Forward/backward pass size (MB): 0.00\n","Params size (MB): 0.00\n","Estimated Total Size (MB): 0.00\n","----------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"AUdgkiuRBZIo","colab_type":"text"},"source":["## Training"]},{"cell_type":"code","metadata":{"id":"SiqIcs6FBo70","colab_type":"code","colab":{}},"source":["from torch.optim import Adam"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UmOydQRnBYqc","colab_type":"code","colab":{}},"source":["LEARNING_RATE = 1e-2\n","NUM_EPOCHS = 10\n","BATCH_SIZE = 32"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"qg8YRdv9AZ6p","colab_type":"code","colab":{}},"source":["# Loss\n","weights = torch.Tensor([class_weights[key] for key in sorted(class_weights.keys())])\n","loss_fn = nn.CrossEntropyLoss(weight=weights)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NFwrk5E2AZ2g","colab_type":"code","colab":{}},"source":["# Accuracy\n","def accuracy_fn(y_pred, y_true):\n","    n_correct = torch.eq(y_pred, y_true).sum().item()\n","    accuracy = (n_correct / len(y_pred)) * 100\n","    return accuracy"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dVA58YPNc2_f","colab_type":"code","colab":{}},"source":["# Optimizer\n","optimizer = Adam(model.parameters(), lr=LEARNING_RATE) "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"bpBgduAbadfa","colab_type":"code","colab":{}},"source":["# Convert data to tensors\n","X_train = torch.Tensor(X_train)\n","y_train = torch.LongTensor(y_train)\n","X_val = torch.Tensor(X_val)\n","y_val = torch.LongTensor(y_val)\n","X_test = torch.Tensor(X_test)\n","y_test = torch.LongTensor(y_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"47LEsByWc3bp","colab_type":"code","outputId":"2eeaf0fe-5e96-4850-f9db-ffe12a276b12","executionInfo":{"status":"ok","timestamp":1583943908169,"user_tz":420,"elapsed":510,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# Training\n","for epoch in range(NUM_EPOCHS):\n","    # Forward pass\n","    y_pred = model(X_train)\n","\n","    # Loss\n","    loss = loss_fn(y_pred, y_train)\n","\n","    # Zero all gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","\n","    # Update weights\n","    optimizer.step()\n","\n","    if epoch%1==0: \n","        predictions = y_pred.max(dim=1)[1] # class\n","        accuracy = accuracy_fn(y_pred=predictions, y_true=y_train)\n","        print (f\"Epoch: {epoch} | loss: {loss:.2f}, accuracy: {accuracy:.1f}\")"],"execution_count":39,"outputs":[{"output_type":"stream","text":["Epoch: 0 | loss: 1.24, accuracy: 18.5\n","Epoch: 1 | loss: 0.96, accuracy: 58.1\n","Epoch: 2 | loss: 0.82, accuracy: 54.7\n","Epoch: 3 | loss: 0.75, accuracy: 54.2\n","Epoch: 4 | loss: 0.73, accuracy: 53.6\n","Epoch: 5 | loss: 0.74, accuracy: 54.6\n","Epoch: 6 | loss: 0.75, accuracy: 54.6\n","Epoch: 7 | loss: 0.77, accuracy: 54.2\n","Epoch: 8 | loss: 0.78, accuracy: 53.8\n","Epoch: 9 | loss: 0.79, accuracy: 53.5\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_gIcdLFeCLR_","colab_type":"text"},"source":["## Evaluation"]},{"cell_type":"code","metadata":{"id":"TyfL-k6uBzMW","colab_type":"code","colab":{}},"source":["import itertools\n","from sklearn.metrics import accuracy_score\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import confusion_matrix"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"JcfmjyWHBKeA","colab":{}},"source":["def plot_confusion_matrix(y_true, y_pred, classes, cmap=plt.cm.Blues):\n","    \"\"\"Plot a confusion matrix using ground truth and predictions.\"\"\"\n","    # Confusion matrix\n","    cm = confusion_matrix(y_true, y_pred)\n","    cm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n","\n","    #  Figure\n","    fig = plt.figure()\n","    ax = fig.add_subplot(111)\n","    cax = ax.matshow(cm, cmap=plt.cm.Blues)\n","    fig.colorbar(cax)\n","\n","    # Axis\n","    plt.title(\"Confusion matrix\")\n","    plt.ylabel(\"True label\")\n","    plt.xlabel(\"Predicted label\")\n","    ax.set_xticklabels([''] + classes)\n","    ax.set_yticklabels([''] + classes)\n","    ax.xaxis.set_label_position('bottom') \n","    ax.xaxis.tick_bottom()\n","\n","    # Values\n","    thresh = cm.max() / 2.\n","    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n","        plt.text(j, i, f\"{cm[i, j]:d} ({cm_norm[i, j]*100:.1f}%)\",\n","                 horizontalalignment=\"center\",\n","                 color=\"white\" if cm[i, j] > thresh else \"black\")\n","\n","    # Display\n","    plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gdzQ8LVuGJL3","colab_type":"code","colab":{}},"source":["def plot_multiclass_decision_boundary(model, X, y):\n","    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n","    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n","    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), np.linspace(y_min, y_max, 101))\n","    cmap = plt.cm.Spectral\n","    \n","    X_test = torch.from_numpy(np.c_[xx.ravel(), yy.ravel()]).float()\n","    y_pred = model(X_test, apply_softmax=True)\n","    _, y_pred = y_pred.max(dim=1)\n","    y_pred = y_pred.reshape(xx.shape)\n","    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n","    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n","    plt.xlim(xx.min(), xx.max())\n","    plt.ylim(yy.min(), yy.max())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Au0tESHKC8Rj","colab_type":"code","outputId":"06c857ea-1d5b-4356-a657-3cda5d66d6a9","executionInfo":{"status":"ok","timestamp":1583943912211,"user_tz":420,"elapsed":414,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Predictions\n","pred_train = model(X_train, apply_softmax=True)\n","pred_test = model(X_test, apply_softmax=True)\n","print (f\"sample probability: {pred_test[0]}\")\n","pred_train = pred_train.max(dim=1)[1]\n","pred_test = pred_test.max(dim=1)[1]\n","print (f\"sample class: {pred_test[0]}\")"],"execution_count":43,"outputs":[{"output_type":"stream","text":["sample probability: tensor([0.0030, 0.0398, 0.9572], grad_fn=<SelectBackward>)\n","sample class: 2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"pKE1ZfJRb1b4","colab_type":"code","outputId":"5cb1bee7-870e-491f-aa1d-e885cdc2d744","executionInfo":{"status":"ok","timestamp":1583943913800,"user_tz":420,"elapsed":1397,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Accuracy\n","train_acc = accuracy_score(y_train, pred_train)\n","test_acc = accuracy_score(y_test, pred_test)\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":44,"outputs":[{"output_type":"stream","text":["train acc: 0.53, test acc: 0.53\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"M3NVCzLWsCe3","colab_type":"code","outputId":"d1478321-2247-4642-9bdf-e4a43cf6e826","executionInfo":{"status":"ok","timestamp":1583943914015,"user_tz":420,"elapsed":944,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":476}},"source":["# Metrics\n","plot_confusion_matrix(y_test, pred_test, classes=classes)\n","print (classification_report(y_test, pred_test))"],"execution_count":45,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAATgAAAEhCAYAAADrprw5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.51      0.59      0.55        75\n","           1       0.50      0.43      0.46        75\n","           2       0.59      0.59      0.59        75\n","\n","    accuracy                           0.53       225\n","   macro avg       0.53      0.53      0.53       225\n","weighted avg       0.53      0.53      0.53       225\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gGfevv1kusw8","colab_type":"code","outputId":"ac0f6bd3-aace-42a4-cd7f-7ef7e1f5d9db","executionInfo":{"status":"ok","timestamp":1583943914669,"user_tz":420,"elapsed":1010,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":336}},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":46,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsEAAAE/CAYAAACnwR6AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy9d5Aj2X3n+XmZsAWgvHfdXV3te0yP\nH3GoaTqdaEbmVnsyp9OtdBtc7Ul7kkK6iIuLkHi629vd2D2tIijFistd8SSetJRCZkWOliuRQ47h\nkJxhD8f0tO8u1+UtqgreZL774wEooJBAmS6HqveJqKgCMpF4KAAvv/l7v9/3J6SUaDQajUaj0Wg0\nRwljvweg0Wg0Go1Go9HsNVoEazQajUaj0WiOHFoEazQajUaj0WiOHFoEazQajUaj0WiOHFoEazQa\njUaj0WiOHFoEazQajUaj0WiOHFoEa44EQghTCBEVQvTv91g0Go1Go9HsP1oEaw4kOcGa/7GFEImi\n2//9Vo8npbSklEEp5f3dGK9Go9Fodn7uLjruG0KIn93JsWo0rv0egEbjhJQymP9bCDEK/GMp5UuV\n9hdCuKSU2b0Ym0aj0Wic2ercrdHsJzoSrKlJhBD/XAjx50KILwkhIsDPCiGezUULloUQ00KIzwoh\n3Ln9XUIIKYQ4nrv9J7nt/1UIERFCfFcIcWIfX5JGo9EcenKpab8phBgWQiwIIf5UCNGY2xYQQvyZ\nEGIpN4+/KYRoEkL8DvAk8B9zEeXf2d9XoTksaBGsqWV+HPhPQAPw50AW+BWgFfgA8MPAP6ny+J8B\nfhNoBu4D/9duDlaj0Wg0/AbwQ8BzQC+QAX43t+0fo1aoe1Dz+C8DaSnlrwNXUFHlYO62RvPAaBGs\nqWVel1K+KKW0pZQJKeUVKeWbUsqslHIY+DzwfJXH/6WU8i0pZQb4U+DRPRm1RqPRHF1+EfjfpJRT\nUsok8NvATwohBEoQtwEnc/P4FSllbD8Hqznc6JxgTS0zXnxDCHEW+B3gcaAO9fl+s8rjZ4r+jgPB\nSjtqNBqN5sHICd0+4KtCCFm0yQBagD8EOoG/FEIEgS8CvymltPZ8sJojgY4Ea2oZue72vweuAYNS\nynrgtwCx56PSaDQaTRlSSglMAh+WUjYW/fiklAtSypSU8reklGeBHwT+IfBT+Yfv17g1hxctgjWH\niRCwAsSEEOeong+s0Wg0mr3nc8C/EkL0AQgh2oUQL+T+/qgQ4rwQwgBWUXUedu5xs8DAfgxYc3jR\nIlhzmPh14H8EIqio8J/v73A0Go1Gs45/DbwEfDPn7PMd4LHcth7gy6g5/BrwVdbm8d8Ffk4IERZC\n/Ou9HbLmsCLU6oRGo9FoNBqNRnN00JFgjUaj0Wg0Gs2RQ4tgjUaj0Wg0Gs2RQ4tgjUaj0Wg0Gs2R\nQ4tgjUaj0Wg0Gs2RQ4tgjUaj0Wg0Gs2RY186xrW21Mvj/R1V94lYFstpSEUMQOAxzL0ZnEaj0VRh\nfuL2gpSybb/HsZdsZs7eDLawmYyreV3P6RqNZq+oNG/viwg+3t/BlZf/7Yb7Rb1xPnNFcu8VP4Yw\n6Q7U78HoNBqNpjJ/8OvPj+33GPaazc7ZG6HmdBh+NaDnc41Gs2dUmrf3RQRvlmCqjt9+Ms5nSHDv\nFT9TsdUjO3GahuQDD8GzF8EQcOUWvPYupLO6K7BGo6ktbGnt9xC2iORkNzx+FoSAd+7AnXHQXdk1\nmtrmQItgUEL4hYFVXiTD8KtHc/lMCMk/+wno7wCvW93X3gRPnYN/8yVJRgthjUZTI7wwkOVFktx7\nJVwjK3ySn/wIPH0e3CYg1Nx7bRj+6KsSqYWwRlOzHHgRrIGHBqCvfU0AA3jca0I4kZJ8/BloqYf5\nFXjjOrw/BAsrenLWaDQHh2Cqjida4kBeCPsO/ArfYC88fa50/vV51Lz80Em4OrR/Y9NoNA9GDYlg\nqwaX0Damvk7y7EXoaoGVmBKyrQ0wNgvfeAvmlgUPn1ST7nrcLvj4M1DnW5uge9vgHzwPP/YcTC1K\n/sNXYCmixbBGozkYBFN1XA4CA6u8WAOpbk+dU3PterweeOaCFsEaTS1TEyL4iRYXa5GDWllC25jj\nXZJf+YdqiU0IkFLdLwR0t8GTZ+H3/koWtgkHLdsYLL9fCHC5oKcVfvW/g898QSKlFsIajebgcDlY\nXxNC2DTAqGAmamqTUY2mpqmJr7BaQnPxwkCWwctJbGkxFVvd72E9IJJ/+mPgca2JWCHW/jYNFWn4\nmY/B9OL2nsE0VZT4TP/OjFij0RxshBB9QoiXhRA3hBDXhRC/4rCPEEJ8VghxTwhxVQjx2H6MFZQQ\n/u0nBYOXE9gyeyDn9bfvQDJdfn8yrQqUNRpN7VITIhjyS2j1OSGcqHkh3NUCAd/G+7U3wfgcWNvM\nBDEMlV6h0WiOBFng16WU54FngF8SQpxft8/HgVO5n08Df7C3QyxFuQCJXIDj4AnhGyNwd7xUCKfS\nal5++87+jUuj0Tw4NSOC8yghbNW8EK4PbH7f0WlYjIBtl96fysDiCmSrCGQpYXphe2PUaDS1hZRy\nWkr5du7vCHAT6Fm3248CX5SKN4BGIUTXHg+1hLwQ/rVfiGHLLBPR8H4OpwSJ4N9/Bf70a3B9BG6N\nwZ99Az77l2DbOs1Mo6llak4EQ20soVXD7ZJ8/OmN95MSZhYhlRH8/l/CbFhFIxJJyGThyk34nT+D\n+WVIZ9ZyivNksjAfhqGp3XkdGo3m4CKEOA5cAt5ct6kHGC+6PUG5UN5z8mlvv/YLMUAeLCEsBW/f\nEfy7/yz4vb8SfO+m0AJYozkE1ERhnBNrjTRqw2ant01y7rhaRuttg2OdzoVuxQgB7c0Q9EuWIoJ/\n/seSvnYVRZ6Yg5WYOsA//2PJyR547DRcOg11XmXhfnUIvvQSaEN3jeZoIYQIAn8F/KqUcltRAiHE\np1HpEvT37k2X6LyF2q/9QowXh12HqhBao9EcPGpWBMOaEH5rIMbvfiHARDRMb7Bpv4dVgkDycz8M\nj5xSxW5CqI5vGwngAhIePwOvvquONj7n/CxDkzA0CX/xsiTgg3QW3URDozmCCCHcKAH8p1LKv3bY\nZRLoK7rdm7uvBCnl54HPAzxx6ZRcv323qEUvYY1GU5vUZDpEMQd5CQ3gyXPwyKDy8XWZa0J4s7hc\nmyugW0MQSwotgDWaI4gQQgB/CNyUUv7bCrt9Bfi5nEvEM8CKlHJ6zwa5CUoLoffHEUgg+fDjkn/5\nTyS/96uS3/xHkodP7tm1gEaj2QNqXgRDqRAevJxkIho+MHnCzz+qrM42w/qcXlC5vvfKYjQajUbj\nyAeA/wH4sBDi3dzPJ4QQvyiE+MXcPl8FhoF7wH8A/ud9GuuG7Kcj0D+4DJ/6AZV+ZhjQ2Qw//wnY\nw6C4RqPZZWo6HaKY/VpC87gkT5yD452qCO2NGxCJr0VhnTq9OZFKgzBUpDhvwJ7OKBueO+PVH6vR\naDQAUsrX2aAIQEopgV/amxE9OJeD9TzxZJzP7GFTjYBP8tzD5Z3iPG748R+Ed+5KdK2FRlP7HBoR\nDOvbce6+EG4KSf7Xn1aRXp9Hefm+8AH4zjXJi9+GWFJwdRhaGpzbboKK/kbi8DffgrsTKvJw4YRy\ndvj2Vfj6W6AnW41Gc5RZK4RO7EmAo7tNzcFO83ZjSDU5Smd37ek1Gs0ecahEcJ69asf5Mx+FoF91\nZoO13889DI+dgf/nS5JvvAXPP1K57XEmC//yT2A15/Twxb/b8WFqNBpNzbO+EHo3hfBqVNVwOJG1\nILPN5kUajeZg8cA5wZtp07kflHoJ73wumcuUnOlfE77FCAF+jxLJxzrX7itGSrBs+OtX1wSwRqPR\naCpTXP+xm001ZsOC6UU1RxeTzqgVOin1nK3RHAZ2ojBuM20694W1dpw711Sju1XyC5+U/NY/UsUS\nlTAMONkD/9MnnQvjhFBJDj/+PJzo0oUWGo1GsxmcCqF3g899WTUrSqYhkVLpDzdG4cuv78rTaTSa\nfeCB0yFy1jrTub8jQoh8m84bD3rsnWAnl9BOdEn+2U+A21wTwJXSHEDdX80ZwjDAa8BPfgT+1Z9s\na0gajUZz5CgvhN75phqrMcG/+P9Ug6Lmepich4UVHQHWaA4TO2qRVqVN576yU0toP/kR5fdbHAEW\nwtnarJo4Xk9Pq2qlrNFoNJrNsTdewoLxOcF794QWwBrNIWTHCuM2atO5Hy04i3Fqx1mpu5zLlDx5\nTrUhTqXhO9eUTVlPq/OxLTsnjKX6vRUBDGDL8twzjUaj0WzMEy0uEJIXyTD8aoVqNo1Go3FgR0Tw\nJtp07lsLzmI2s4Tmdkl+46egrXEtleH8cXjzhhKrTqHzrAV/9CKc6oNnLlTv8LZeIGctuDoEtq2j\nDBqNRqPRaDR7xU64Q2ymTeeBYf0S2no++DC0N5Xm8no9Stzevq9EazG2hHgS3h+Gv35VOKZGFPa1\n1U8yrcRwMgWLq/BnL+3Qi9NoNJqjiLQA7Vum0Wi2xk5EgvNtOt8XQrybu+9/l1J+dQeOvWs80eLi\nxeHy+586r7oCrcc0YWpeFUg0FZmlWzZ87m9go4YWUsLCCnz2L+BMvzrOxDxcGwZb2+1oNBrNtsiv\n8L04LAsuQLvdUU6j0RwOdsIdYsM2nbWEXSk3V0IqA//3F1V6RG8bhCPwzl3IZNXLbwzKim2SbRv+\nz/8XJII3DoRvhma7WBmLZCyDx+/C7T2U/WY0mppivQvQRLRyzYdGo9HkOeJn8PLIwXevQWeLcoEo\nxrKV4JVScH0Ero+UH627VZmpO3UaMozKThKa2kBKyb03xlm8v1b32dQdYvDZPkzXjhqtaDSaLVJc\n/KyFsEaj2QxH9sy91kgjWdJI4zvX4P6sytsFFcFNZeClt2BmqXrAe3HVuYMcQDyl0x5qnVuvjpYI\nYIDwVIQ7r4/t04g0Gk0xTo00dt42TaNRZNMWs/eWGH1nmrmRMHMjYW68PMK1l4aZvrOAndW2Twed\nIx0JrrSE9tm/kFwcgEdPKYu0N27A2MzGAnZ2STA1L+nrKI0GpzLwjbd28YVodp1ULM3KbMxx28ps\njFQ8g7fOIZlco9HsKeUuQD6dJ6zZcWLhBDdeHkHaEtuSKim0aKU3vpxgfjjMhY+e1CuFB5gj/84U\nRw5AMhENY0vB1SHBF/9O8OffFJsSwHk+92UYnVZpEfEkZLLwxnX42pXdew2a3Sc8Gam6PRVL79FI\nNBrNRuxNIw3Ng5KKp1mZjZKK19b8KaXkznfGsTK2EsBQIoABbEuSjKaZH9mdtt6aneFIR4LzODXS\n2HoLTsnT5+FjT0J9AMbn4Hs3VB5xLKnTIGodw1X9PfQFq/THrkI6kWH69iIrM1HcPpPO0600dYe2\ndSyNRlPK5WA9DKzyIgnuveLXEeEDgpW1uffGOOGpCIYpsC2p6iueqY36imQkRSaR2XA/25IsjC3T\neaplD0al2Q5aBOd40CW0H30Onn90zV/4RDf0tcPMEtyb3L1xa/aGpu56EFNlV/sAvpAHj3/rqRCp\nWJr3vzaElbWQNrACkYU4XWda6Xuo48EHrdFouBys54kn43xGC+EDw703xgura3ZWTarhqQjDVyY5\n9Wzfrj9/YjVJeCqCEILm3nq8ga0FMexsvuvVxpXuhnnwRf1RRr87RWx3CS3gk3zosdIGG4ZQfsM/\n/8ldHLBmz3D7XBx/rKusHbbpNjj/oRNbPp6UkuErk2TTOQGcw7YkU7cWSG8iyqDRaDbHWiF0oqQQ\nWrP3ZFJZ5/QyCYvjK2TTu9f0RErJ6DvTvP+1IcavznL/6izvfvUuU7fmN/X4bNoiFk7g9rvKzgVO\nGKagfUA7lBxkdCTYgeIltKFX/Bj+KC3eAPPL4GSJfKyzsr9wQwD62iXjczol4qAQX04yfWeBZDRN\nqLWOzlMtm4rkdg62UN8aYHZokVQsQ1NPPW3HG7d8pZ+Kpbn56hjJSMpxuzAEKzNR2k7oyVOj2SnW\nF0LriPD+EJ6qUl8hlUh2eSrYLD0gKzNR5oaWivJ41e+Ja3M0doaoa/Q5Ps62JaPfn2J+dBnDENi2\nJNDsIx5Orh1rHYZp0NAZoKWvYVdei2Zn0CK4ApeD9Tz9dJb00yE8wkAgicQFX/gvkvuzpYI2nnT2\nBs7zzAWVI6zZfxbHVxh6cwLbliAhuphg9t4SFz86gL/eeQIspq7Rx4nHe7b9/FJKbr02RjLqLIDz\n6CU0jWbn0V7C+082ma263buN1LLNMntvyVG02pZkbiTM8UtdpOIZwpOrSKnylH1BL/ffnWFhbBlp\nSyxbPT4WTlLfHsA0DRKRFMFmP43dIaILcWxb0txTT317ALGZkLFm39AiuBKGgd/ThL/oA+zzwP/y\nE/B/fEESTazdPzqjmmk4eQQLAX7vXgxYsxG2ZTP8vcmSSTA/qQ2/NcWFDw9s6XiZZJZUPIMv6Nl0\n5CK+nFROEtVSyaSksSu4pbFoNJrN4VQIrYXw9pBSkk5kMExj090zfSEvwqAkDSyP2+/C2MXCuGym\ncqqFlbGYvr3A/auzBbuz+1dn6TzZzNxwuXiWlmR1NsbjP3IGV9Frb+mtHPlNJzLElpNYGYtAo29T\ngRfN7nL0RLBpAAKsDfKOPM5Xo4Yhefai4OsllmeCr1+RfOJZyvKEUml4796DDFizU0QXExW3RRbi\n2JaNYRqkYmmmbs6zMhfDG/DQc76N+rZAYV8ra3Pn9TFW52II00DakrbjjZx4vBthVL/qTyeyap+K\nS2hCdaBz785yoEajcSqE3o4j0NEmPBVh5K1cXYOEYIufwad7Nywya+wO4fK4yKyLCAsBJx7v3s0h\n09xbT2wpUSZoDZdBXYOP8fdnkXbptpl7izilQQIYhiCVyJaIYCfiK0nufmecxOraCqAwINQa4Mxz\n/Xq+30eOzpqraUJ9EIIBCNZBQwjcVT64LrNc0QJet6CpsdzT8OtvKSeIbJG2TmVgcgHeH9qJF6B5\nUDYSqEIIYssJ3v3qXWaHwiQjaVZmotx4eYTp2wuAKox458VbrMzGkBLsrI20lQ3O2HszjsdNrCaZ\nubvI/EgYX9BdMYfM9Bg8+snTNPfs/InYtmwSkdSuFp1o9h8hxBeEEHNCiGsVtl8WQqwIId7N/fzW\nXo/xoKC9hLdPdDHO3e/cJ53IYlsSaUsiC3Guf2MY26reJU0IOPZoJ6bHAKEu/A2X4Nilrl2Z+4pp\nP9GEx+8uORcYpqCuwUsqlnGcm6WtIt5O2LbcsElSNm1x/RvDJQI4f9zV+RjDb01hZe0N/2+a3eFo\nRIKFUMJ3vait80M0pnIZinGZgFBJ8+seY9k2y3VJJqLxkiW0TFbwb/6T5PIlePKcKpT77nX41nu6\nXfJ+k01bICXBZr+zEBbQ0BFEGII73x4viwQgYezdGQy3wfzoMtl0+WRlW5K5oSX6H+7AMA3S8Qxj\nV2dYHFsp7GOY6rlDbXUqb6xowjVMweDTvduyWquGlJLp2wtMXFfVz9KWNHaHOPlUDy63iW3ZTN6Y\nZ+beIlbWJtRcx/HHugg0+Xd0HJo944+A3we+WGWfb0kpP7U3wzn4aC/hrTN5Y75cMErIZmzCkxFa\n+p1TAqSUDH1vkqXxlcLjpZQ09zTQMdi828PGdJtc+OgAY+9MszITxXAZdAw203mqheG3pio+zuN3\nk01ly+bsthNNG6bCzY+EVQ2KExIW76+weH8FIaChK8TAE90l54HEaopEJIU/5MVfr3Mrd5qjIYLd\nVYSF1wvxomVyvxc8ueWc9aJZSgwBLb4Ig5fNsiW0VEbw99+Dv//eDo9fsy2S0RRDb04SXYyDEPhC\nHnovtnP/vRmkVILQMAWmy2DgiW6klKSilTsXjVypPEmCEsI3XlFtNGNLScftANGFOJ2nW5gbDpNN\nW/hDXvof7aSpa2eaZMTCCe6/N0NkIa6u5SxJcSBjeSrCndfvc/5DJ7j9+n1WZqKFbZGFOO9/bYjz\nHzpBfXvA4eiag4yU8jUhxPH9Hketob2Et0Z8pXx+A7Uytj7iWczKTLREAIOKiIYnV1mZidK4Q3Ng\nJZKxNDdfHiGTUitiVjbLymyMzsEWmrpDLE2sYmdLgxyGKeg83YyVtpm+s1hwlGg/2cyxRzo3fM74\nShJZYfWvGClheTrCtZeGefQTp7BtyZ3X7xNdjCOEQEpJoNnPmQ8ew6XTJ3aMoyGCDeGY2oAQYBRl\nhBiGEsAO4rfwkFSG5wIhsgOrui/9HpNOZlmaWMHOSho7gxXtbEAVQFx7aZhsbrJDShIrKcbem+Hc\n88dZmYmSjKUJtdTRdrwRMxcVfVCiC5Xzjovx1nl44sfOPfDzrSe+nOT6N0fKJvJipC2JLsZZHF9h\ndS7quM+tb43x5H97Tlc2H06eFUK8B0wBvyGlvO60kxDi08CnAfp72/ZwePtD3kJNCWE9r1fDX6/S\nB9ZjuAx8oco5wfOjyxXdGeZHl3dVBEspuf3aGKl4pqQweXUuxth7Mxy71IU/NE98JVVYDRSGwO1z\n0THQjOk26TnfRiaVxe11bdrBx1/vQ5hiU0IYqVYulyZXWRxfJbIQz40lF0BZTDD05gRnnju21Zev\nqcDREMGW7ZjagJRgZdX9UlbOES5+nNcDpsll0Etoe8j8aJjhK1O5t0oycU3Q0t/AwJM9jkJtYXQZ\nyyH/VVqS8fdnGHymD2+dh0RECeNYOEk2Vd26Z6ewbVm1SlnaEoTKUU5GUiRjGXwhD6lYGitjE2qp\nw+1z/qyOvz9bVQDnyXsRO1Vog4rorM7FaOjQLhWHjLeBY1LKqBDiE8DfAKecdpRSfh74PMATl05t\n4gxe+2gv4c3Rc76d1blYmaA1TdWBrRJlqWab3LYTJFZSjs480pbMj4Q5/lgX5z88wPSdBeZHlB1a\na38D3efaCoVrhmngrdtad7n2E41M3pjD2owIRs29kYUEy1ORsv+JtCXL01GyqcrFeOHpCONXZ0lG\nUnj8brrPt9F2vFEHNCpwNERwJgMyl0uz/oPg8agf24bMJkSQECpn2GVyOVhP10MW7kf8xMMG33wv\nzt1hv84BfkASkRSzdxdJrKYIttTR1BNi+K0ppC0L85dEsnh/hYaOIK3HGsuOEV2KU6GWgch8gnf+\n9g6eOjeZRFYVPezhKd4whGOqwcpslNG3p0msphCGwOUxyaaVm4Rq06kmYWlLuk630PdwR9nEFlmI\nb2oM0pYb5pclVlNaBB8ypJSrRX9/VQjx74QQrVLKhf0c10FCewlvTKi1joGnehh9e7pQGFdX7+XU\nD/RVjZC29DewPB0tW3UzTFExj3inyCQrO/PkX4PpMug9307v+fYde16X18X5D53g3hsTJCOpwnmp\nkk2cYQrcXrPidmEIMmkL25Zkkll8IS9mzlau4IOfe43JaJrR70+RiWfoubBzr+kwcTREMCjxul4A\nF9/Op0JsFpcLfF7OmDkXiQYY7HZxczzLH37FhdRCeFssT0e48+37hWYWq/Nxpm4t4KRobUsyc3fR\nUQRvmDMlIe2wnLfbiJwADjarwrN0IkN8OcHM0BLLk2upCTI3weX/zo85H+WdubtIXaOv7LWbbmND\nBwhhCIItdXQMNjP2rrOjhTBE1WVNTW0ihOgEZqWUUgjxFMohaHGfh3XgOApewplUlsnrcyxOrCKE\noO14I91nWzdt19Xa30hTd4j5kTBWxqaxK4QvVP3CurmnntnWpZLCYMMUBFvrdt0Zoq7JV9GZxxf0\n7GqDokCTn0c+fopULI20Jd6gh8hCnFuvjpaNSRiC9pNN6rznEJ2RUjLy1hSRhTiGoXKFu8620nO+\njbF3psuOZ1uSyZvzdJ5u0VZsDhwNEbyZbhUi547tlDbhhCGU7VpJMw3B2V6Trq44U1O6qGirSFty\n740J1jezqIaVKb1Utm3J0vgK0bBz4cZ+IkxB38V2Ok+3ko5nuPvdcWJLiYoR62rYlmTq1kKJCLYy\n1loOdBUaOoMMPtOLYRoce7TTUQh7/C4dBa5BhBBfAi4DrUKICeAzgBtASvk54CeAfyqEyAIJ4Kdk\nJf+nI85h9hLOpi3e/9oQmWSmEG2curVAeCrCxY+dxNjAThJUAe7NV0axbYmUkskb84TalO9tJUEp\nDMG5HzzO4vgK8yPLALQdb6Slv2FDC8sHxe110XmquaxrnGEqe7a9oNhDub4twPHHuxl9exqkLDhI\nuDwms/eW6D7bwtTNhbKxurwuIgsxpE2he930rQUM0ygU/K1HGIL4SopQa90uvrra5PCL4ErFbk7k\n99mMEHaKLAN+r+DZcya/d1fnkm2GdDLL8tQqUqp2mRWtZBwQhqCpJ0QmlS1cFY9fmyOxUrmf+37i\n8bvpPtuGbUuuf2OYdDL7QGkYxWbzUkomrs9t7NGZS7PIR8q7zrQiTMH9d2cKYjzQ7Of0s306h6wG\nkVL+9Abbfx9loabZBKVCOMPwq4cjkjY3vEQ2lS1Zbpe2JBlNszSxSusGqQnSVu3fi1edJMr3duL6\nHP0PV3ZNEIag9Vij4wrebtP/SCfeoIepmwtkUlnq6r30P9xJQ+f+XPC3n2iipa+eGy+PEl9OIm1J\nKpZh6uYCHr+Lvoc7mL61QDqRxeN30XaiienbC2VpErYlmblTeUHHtiVu3+H47O40h18EV2uI4US+\nSK5acCSerHhcKSW+Nm16vRlm7i4y9u5M4VrCtmVV4WWYoiBuhaGumG1L8vZXbmMYQkUkdrm4YkNy\nBW1O40jHVfrF8tSqKox7wKEGW/zKZD1jcfv1+0TDiQ2PKW2VS33yyZ5C5KVzsIX2E00ko2lcbhPP\nBubvGs1RIpiqA2q7iUZe4BqmwBvwEJ6KOLs0ZG2WpyMbiuDV+ZhjAa60JLND4aoieD8RQtA52ELn\nYMt+D6VAZCFBYjVZcs7Ip8NJW/LYj5xFSnVuXMqlrjhN9JlkltZjjSyOr5SefwTU1XvxBbXHsBOH\nXwRvh7wQTqVVwVzWUqJXSlU8lxfJbldZNNiWku/PJdH/2urElpPKr7eo2A0qd+YJtvipa/AxP6qq\ndj1+N/VtAWbuLoJcWxbaT4QJJx5Ty1tOo8l3FkpE0qrQ7YGeS5CMprny1ze2LKZlbunNLOmapNqG\najSaw0V4cpWhK5OquyUq/8GYkesAACAASURBVNVTwV0GAW6vSTKSIjwdRRgqj3d9E59qdQd2Fecb\nTTnhqVXH84FtSRbHV+k+21YIDvkbvNgVzpHegJsTj3eRiqWJhXNWnULg8bu0pVoVDr9Sy2TB53AF\nlBeyFdIakBKy2bU+yKl1TRSyWUhnwLM2OUgkU7EYo6spbCm0vU4V5oaXKqcs5N4SaavorzANGtqD\nTN9ZKFzhpmIZ5mPLezjijZEWjL49jS/kzV3Zr20zTEHvQ6o61x/yqlb01cSrAJfbwLZVdEaYIpey\nLgk0+lQXoZXKpvTV8AU8hWpijUazMU+0uHhxOFtorVwr83p0KcHd746XzLWJlRTpWMbRfUAIJXDf\n+7t7hdtj78xw7FJnSfQ01FJXMXUt0KzzTreC6TIqng9c7tJ52h/yUt8aYHU+VhLtNUxB78V21RHv\nIwPElhLEV5J4Ax5CbXU6ta0Kh18E2zak06V5wVKq+yMxCAVUgdt6hChvp7yeRFIJ4VxqhMhk6LMk\nLwxkdSONDSjOZ11PqNVPqDVAMpIm2Oynpb+B9/7r3b3J8xVraRbYEgyBy21guAySkcrd5PLYtqSu\n0Yc34GZ5OqqusQxB38MdtParHLjG7hCmyygr6isew5kP9NPYrYzjpS0RhihMZPffmyG+TQEMqmvS\n7W/fx3QZRBbiuH0u2o43EGoN4A95d71ARaOpNWq1kcbUTYf2xqiATUNnkJWZWMlpsf1kE/PD4YLA\nyj/y/rszNLQHC7aKnjo37QNNqiVwWZHZwUyFOKi0HW9i+s5iWTMNwxS0nyxvJX36uX5G3ppicVy1\nWjZMg76HOmg7vuZeEmj2E8g5EGmqc/hFMCgxY0sQci3NIZ2zx0qkIOAvjQZLqYTzZoqmLUv9FKF7\n0W9MU1eIZYe8NMMUNPc20HW6tXBfZD5W0d9xx5FKBA882UN8WV1JN3YGufKfb2768YnVFA997CTZ\ntEU2ncVT5ymptjYMwbnLx7n29eHyxws49/zxEmcGYa7zAl6Mbzr3uaEjQDKaLu3uJCE8sZbjmIqm\niS7EEQJMt8mxS50lE+pWiC0niYUTeP1u6jsCOgKhOTTUYiONSi2M7azEH/Jy4rHuwsV6U0+9Sptw\nyhXONZToL2oTfPyxLuoavEzfXiSTyhJs8dP3UGfB/lGzOfz1Xvof6uD+1VlAFlZAW/obHBuPmC6D\nwWd6OfFEN1bawu1zFQIX2YzF/HCY5ZkoHr+LzsEWLYY34PCIYI8b3G61vpPKKGEqhIr0Fqc8SKmi\nwnkRnM1CLA4+H5iG2p5Mq+WJQN1aJHmLLXWPYi/6xfEVpm4tkElkCLbW0Xuh3THP1MpYeOrcuLyu\nQvJ/HpfHpP1EqQAz3eaeFry5vS7q2wLUt63Z3BmmwN5MQzkBdQ0qWuLymLg8zhW5weY6zl0+zu1v\n31eR3tz9J57o3tCazBf0EJnfXFOMyGJiU8YokMsASluMvDWFx+/ekkWanbW5/frYWrMOoVwozn/o\nuC7I0Bwaaq2RRl2jj0QkVbbUbrhUDYA34KFjcC3aWNFiUZbnAQsh6BhsoeMAFZnVKl1nWmnqqWdp\nfAXbljR1hfAGPSzeXwEJDV1B3Os6xJkuoyStLZ3Mcu1r98ikLRVVFrB4f4Vjj3bq96gKh0MEhwLK\nCq3Q/titor3enCdfsQoQQoldt0sJZY9HdYCzshDPABKCRcJZSiWw86kPW6BWl9C2w/i1OaZvrS29\nLY2vsjwV4fyHBwqRgeXpCKPvTJOMpNWVqwDDJbCyEnLXGOlklhuvjHL2g8dweUwk4Am4Md0mtrX7\nbY0NU9B5unzCMFwGbMKD1zAEXWdaN9wPoKEjyJM/dk71h5eSUEudep4qhHM95TfLZloolz3GUnZr\nlURwfDnJ/NgydtamqTtEQ2eQsfdmWJ0vjlBL0lmbW6+N8cjHT+mIsObQUEuNNLrPtRGeXC2N7grV\n3rilr9wBorknRHw54bBCZ9DYFdrt4R5pfEEP3efaAJgbCXPtm8MFJwhpK3u3LodzU57xqzOltptS\nzeWj78zQ0tdQsc3yUaf2/ys+75oAhrXfTgI4jxBr4jd/2zTVffkI8vrj+X1bFsFQe0todtYmGUvj\n9rnKrjwrkUllmbo5XxattS3J6NvTXPzoAGPvTjNzZ7GQYZLft6yfuoTYUoJ3/vZ2yUQs1mnDYru0\nrSAMFcEYeKoHgOHvTULuWgcJbQNNZUtQqVi6YG/mhGGqz4vpMhh4qmdLLgtiXQtlKSXRxQTpRIZA\nkx9fcM1cPRFJlRW57BaV8p8nb84zeX2u0NFvfnSZYLO/YN6+HtURL0mgSS/JaQ4PtdJII9Do4/Rz\nxxi+MkkmpQRSoMnH4DN9jhfc7Sebmbm3RCaRWWvvawr8DV6aurUI3gsSq0lGvz+FtCTF3knjV2cI\ntfgJtjgXHi5NrDoW1wlDsDwT3Rdf5lqg9kWw2125sUWl+6VU0eD1EWIo6wJX8hiXS6VPbJFaWEJT\nzRbmmb41Dzmf28buEINP9WzYajG6qBpVONmURRfjJCJJZu4ubakz2nqht15gdZxqwRf0MPL9qQ0t\nwoQAb9BDsMVPoMlP2/GmQppCY2eoYFHT0BksEZ150ols5UI2Aec/dALDZeCv9z5QxDMZS3PrlVHS\nySwClYfX3BPi5DN9GIZg9u7ilpqJPAj5Aphi4qtJxq/NFqL2oC6aootxRwEM6oIjk9r9CL5Gs9cE\nU3VcDpKr/zi4hdCNnUEufeo06UQGwzBwV7JHQ6VwPfRDJ5m6Oc/i+CrCELSdaKT7dKsumN0jZofC\njvO8bUlm7i0xWEEEV0W/dRWpfZ+k7b65WxUrFQyqN4sSwi5+7RdiDF5OMhENb/tYu8H0ncVCOoOd\ntZG2ZHkqwp1v39/wsabLrPqfmb238691fjhMx8lmjj3aWRYlXo9ERXNbjzXSdbq1JE/X5TFpO95E\nx2CzowAG8Ic8FaOvLo9JoFl5GD+IAJZScuvVUZKxNHbWxsq9B+GpCJPX5wBIRtMP3GBjMximoPdC\ne+F2NmMx/L0Jrv7dvRIBnMe2ZMUTpG1JHQXWHGouB+t5YSDL4OVEwULtoCGEwFvnqSqA87i9Lo49\n2sVjL5zh0idP03u+fcM0Lc3OkU5U7iSaSVRekWzpb6gQv5M0duoofiVq/5NdTXg4hR43E46stE/2\nwUzA80JYTZhKCB+ECVNK6WilI23J6nycZLS6HVeotQ7TVfl9mL23VLEJxnbJpi1SsTQdA80YThZ3\nxUgVSb777fEN2wo74fK6aB9oUmkPRRimoO9ie0H8SilZno5w+/Uxbrwywuy9ReeuSrYkFU9jFW2L\nhZOOk59tSdUQBAi1BcqcIrZEfrHDY1LfWVfxArLlWAPegJvoUoJsxuLGyyPMj61UFeBun8vx/9N5\nqnnTaTUaTa1yOVjPbz8pDrQQ1tQGjZ3BsrkU1Hxarb1z30MdePzutccK9ZgTj3VXLNDW1GI6hGmC\nIda6uG1EviFG/m8ovb3eGs22S/OB84+JJXZk+OW5ZPu/hCZtWbEq2DAFyUi6aoW/MARnPnicay8N\nOQqlXXF2EMoIvqWvgfMfOs6t18YKorJaMdjqXGxbBR7HL3Xh9ppM317EsmzcXhe9F9vpyPk4Slsy\n8vYUC6PLhYuJ6EKcmXtLXPzoyUIV78zdRcbfn1Wd8iS09DVw4oluMolMxUUNK2MjpaR9oInpWwtk\nre1djD3y8UE8PjeGy+DGN0cqitrwRISF0ZVcK2obcG4DnUeYgvaTTdS3Brh/dYb4chK3z0X3uTba\nBw5W2o9Gs1scpUJoze7R2t/A5M150vH0WpqZUMGL9oFy3+A8bq+Lhz9+ioXRMMuzMTx+Fx0nm3Un\n0A04+CLY4851ZRNK/BaL1mpRvWIBu178rt8n/1uixK5tq+d0mcpfOJ1Wv3eI0lyy/bdQE4bA7TXJ\nOAhh25L4HPJD1xNs9uPxu0jH9yb/UxiisLQXaPLz2AtniCzGsTIWd749XlG0bTenVhiC3osd9Fxo\nR1oSYarmFdHFOCNvTxNbKr9Isi1JMppm9u4i3efamBsJc/+9mZKI++L4CplUlpNP9lQcWz7X2O11\ncfFjA4y8NcXKXAyA+raA8g3eRLHc7L2wSh8RAm/Qs2Znto68FZJV5PRQ+f+iLpQSy0kyySyGaeCp\nUy2tGzqCFVNEpJTaMUJz6Ki1QmjNwcNwGTz00QHGr82xeH8FKSXNvfX0PdSxYUTXdBnatm6LHGwR\nHKwrLVRbH7k1N8jmKBbC1YrnLEs5P6SLci7TmW25QWyFg9JUQwhBz4X2MoEmDEFDRwBfwDlXdj31\n7UEWRh+wlXGhY5vt2E89j9vrItS6ViAgDFHw9W3oCLA8HS17jC1liffvtoYnBCKX+hFfSXLj5ZGq\nbg3SksyPLdN9ro2Ja3MVUk5iWJZN67EGFu+vlHdgenTNoN4X9HLu8omCyBeGYHU+xu1vjalc7ipa\neObOIi6PSe+Fdpq6Qw/8XglDfb/srCyzbUtG0yzcX+HiRwaoa/QVXuvE9Tlm7i5iZWx8QQ/HHu2k\nqUeLhEONYay1qT8CHKRC6Fg4wcKYWp1q7q2nvl03sKkFXF4XJx7v5sTj3fs9lEPPwc0JdrvLnRqc\nIrlQfXLd6AsvJWSyyld4H+bo0lyy7L7lknUMNtN7QRVAGKZAGILmvnpO/UD/po/RthNL31It//sb\nqqdfnHv+eMXJ/NilLky3UVIwZ5iC/oc3vpLeCpM35jcVWRZCIKWsaLNmGILEaoqBJ3roOd+Gy2uC\nAH+Dl9Mf6HdM3xCGKBSj1bcFePxHz3L6uX6ae0NVq7gnb84DsDi2spmXWBFlC5fTNk7/A6nSUkbf\nmS7cNXRlkunbCwWXjWQ0zd3vjhOe1PmThxKPGxpCyne9PqiCGkdEgB2EQujx92e5/o1hpm8vMntv\nidvfGuNOrjmPRqNRHNxIsMe1+Qkzk13LD97OJJt29kTdK9ZyyfYvR1gIQfe5NjpPt5BOZHF5TVwb\nWKMVI6Vk+M2JHRmLtCWxxWTF7cFmv6OFVx5/yMsjP3yK6TsLrMzF8Na56TrdWuLHuxNEFuIbXjgZ\nprIYEkKlb2SS5eki0pb4gh6EIeg5307P+XaHI1UmupQgMh/D5TE5+VQv99+fZfbukuO++bSJyOLm\nOs5VoqEzSHgysuF+q/MqbSMVT6ulPQcv6bH3ZnQ0+LDhdilvdSHWCjBNUwnhSGxfh7ZX7KeXcCyc\nYPr2Qsmqkm1JVmaiLI6vaM9YjSbHwRXBm71YtSyI5/IxG6oUPDkVwYF67AG4Ml6fS7ZfS2iGaVS0\nCqtGZD6uutXsNgLHfurr8dS5OfZo164OxRtwb9BEw6CuyVconus53+aQcqJymrdTvGDbkjuvj7E6\nF1M5tobByNvTtJ/Y+ATn9pqOgnyzRB1yoJ0wclHpeDhZ0Us6GUnrHOHDhtfrvHJnGKrW4gGddmqF\n/SqEzqdArMe2JHNDYS2CNZocBzcdIp3eOIdMSkgU2XfFk6W5Z/m/o3HV5CJ/27ZV+sNKREWRDwjF\nS2ggD5yXcDVWZqN7s8wmOTB2L91n2xytbIQhaOlvYPCZXi586ARGLne9Y7CZrrOtGKbqLqe6xQU5\n88Fj23r+6Vsq0m1bqq2mnbWxszZzI8sV7c/y0fCuimPf3HNnEht/b4RQ3pUAHr+7ok2e6Ta1AD5s\nGFU+SNW2HUJUIfTeeglXS9NSji8ajQYOciQ4mytW87idt1uWEsDFdlGZDERt1TLZMJTwzTs75C3O\niovlDiBOfekPWjvOdCLD3NASsXASf72X2HKSldnonuVUT96cp+3E/ltvNXWH6L3Yzvi1OQwhkKjq\n3DMfPEawubxBhBCCvosddJ9tIxlJ4fa58PgrfL43wezQkqMrhAC6z7QydXuh5D1x+UxOf0DleLce\nayAWTjB7b0nlF6PEe9/DHYy9M70jrZnd/rVofF2TD2/AQyKSKhmTYQq6Tle2/dHUKLYFRoXTyza8\nug8De1kI3dxbz/xwuOx7bJhCR4E1u0Yqnmb86izLM1EMQ9A20ETvhfYDHeQ4uCIYIJFUQjif75vJ\nqBOoaShh6+SXWpweUYzXAx6PUgjpTK4Q7mCK4YPiJWxbNqvzMeyspL6tDpfXRSyc4Po3hguTa3hq\n47zQSghDcOEjA1gZi5uvjm5aRKeqpCDsNd1n22g/2Ux0MY7pMgm2+Df8wpsuY0e6qFkZ5yVlKVWO\n8eM/epaFkWWSsRT17QGaehoK6QlCCI5f6qL7bCuR+Timx6ShPQACwlOryl3jAb8e9e11hai9EIKz\nzx/n1qujpGJphCFUxXpfw5ZzoDU1QDIFAbM8Bc22neftI8LlYD1P7IGXcH1bgMauEMvTkTUhLHIF\ntabAtuzCCpVGsxMkIymu/v29kguvyevzzA2HufTJ0wf283awRTCoCTM/aQb84HKt5ffatkp12EjM\nrrda83pUhDmyd9HLrbKXfelty2b6ziJzw2Fsy6a5u576jjqG3pws+UB3nmlhpXhSfUAa2gOFiGl9\nW4DIQmzNHLwKm7Vs2ytcbnNf2lLWtwcci9OkVNvcXhddZ1urHsPjdxdSFkBd1MSWkgVHiwfBWLfs\n7a1z8/APDxJfVt3xAo0+PHXbj4RrDjDZXDAiXxwHKvUskVBBjfw8ns6oefwIsZOF0JlkltF3plma\nWEVKSUNHkOOPdeEPeTn1A30sjq8ycW2WZCRdcN4Ze3uG6VsLXPzoyQOTWqapfUYrrCBmElnGr81y\n7JHdrdHZLgdTmjvh96mJM19ckf8d2CCi5nI5W60JoSLDB5y1XLLdiX5KKbn56iiT1+dIRdNkEllm\nh5e4+52Jsg/0zO1FEqs75KQhwFtUgHf6A/3UtwcRhlB96gW4vGZZjqphCnov6sghQP/DnYX/VR61\n3NmAL6TcM1Zmolz9+3u8+RfXefsrt5i+vVBR3MbCCe5+5z6ZZLZqfnfPuerCOj+OYnGdRwhBoMlP\nU3dIC+BdQAjxBSHEnBDiWoXtQgjxWSHEPSHEVSHEY7s2mEwWVqPqZyWiRHEwAH6/CkR4PRAKVE55\nO8QoISwYvFy9JX017KzNta8PsTiec12R6vt+7etDpBMZhBA0dgRIxUrPHbZlk4plmLg+96AvQ6Mp\nsDJb2fVlfqSyJ302bTFxY45rLw1x67VRwtPbX13eDrUjgj1u52rjfBvlSrjM8sflH+s+epPvelZm\nY8TCiVLBuwfRccMQdAyu5YImVpKkYsolwLZsAo0+znzwGA2dyvfWMAWm2+DYo1209JWLq6OIv97L\nQz90ktb+Btw+F/56L8cudTHwZA8Ay9MRbr8+Rnw5ibQl6USW8fdnS7x7i5m8MV81yu8Lehh8tpe+\nhztLGnisxzANmvsadtySTrMp/gj44SrbPw6cyv18GviDXR9R/qLL51WBi/x8nQ9GFEeLNZsm321y\n/XxtW5KZO4ukExmGrkw6XvRKW7I49oCNjTSaIqp9hSsFXjLJLFf/7i6T1+eJLiZYno5y99v3GXvX\n+Ry1Gxz8dIiNkORK2ivkmeUdIZzeoQOaE+zM7uTRrcxGq3Zm22mEIRACTjzZU7AFS0RS3Hx1tESA\nxcJJbr82xqVPnUbakmzGwlvnqdoI4ijiD3kZfKav5D5pS5amVhn+3mSZqM1bJPWeby+0nc6TWK0c\nleo+20r/I2vCt+tMK43dISauzZGKpfH4XLnCQJPmvnpWZqN8/8u3kLakqaee/oc6dOR3D5BSviaE\nOF5llx8FvijVWekNIUSjEKJLSrn7Zx2nQAaoOdzt2vUOnQcVW25vbq9kgyZtSXg6wuy9JSzLrhjU\nqKnTn+bA09LfUDHi29LrHLiavDFHJpUtSYO0LcnM3SU6Blu2Zde6VWpHBFcSsoLqhRaZjIpAOB0v\ntb9NMjbLEy0uIMvvvpLd8bxgl8dEGGLPugh1nW2l52wrZlEjjunbC46WPrZlMz+6TOepFlze2vmo\n7ieZZJZr3xgmk8xiZ51zLQ1TEAsnyjrR+Ru8jkLYcBmOzUn8IS+nni0V4LZl8/7XhkhG04XP1MLY\nMivTER7++Cnc+n3cb3qA8aLbE7n79i70sp4jfF37wsD2GmmszsVYnau8/JyOZbAqfP8B5bfed3Ac\nhzS1wcpMlMmb8ySjaQKNPnovtBPI1fUcv9RFeCpCNlWqx0y3Qe9DHY7HW5pYrVgHtDwdofNUy46O\n34mDlw7hMlWawnovyUSy/NI1H+X1eStPpLYs9Q/O/6TSykKtBij2D7Zldkf9g1uPNWz9JPQAJy07\nY5UIYFBRX6dohW1J4suVO8dpyhm+Mkkqlq4ogEFFitZHgQF6zjl7BxumoLlCCoptS5YmVpm+s8Dq\nXIzFiVVS8XTpRZWEbNZm9u7i1l+QZt8QQnxaCPGWEOKt+YUHa7MNqEhvpfDjAfJr3yvy87qq+Uhu\nyT94/P3ZypFcAxUBroAwwO1z0XfRWZhoNE7MDi1xO9ecKR3PEJ6KcP2bw8oeFeX3/tiPnKH7fBue\nOhduv4vO0y08+snTeHwusmmLdDJbkhpRaWVXiMrbdpqDE5YxjLXe8hIltDLZNbuzTFZ5/fq9awI5\nn1Pm9ajltErtODMZWC1qrZzNHogucVvByT94JzrKees8nHyyh6Hv5XLHNvNvye1julXDh/VXftVI\nRsuj7/56L7Fwouy5DVNUbY+sKcW2bJanI9XfQwGeOg91jeUd6gJNfk59oJ+RK5Nk0xZSQl2Divaa\nrvLr5UQkxY1vjmBlbaQtVe62IRzTa6QlWZ6J0qtPvPvNJFAcvu/N3VeGlPLzwOcBnrh06sEnzFR6\nrQiu2K89mTqya/OlLkCb9w+Or1QODjR31xOeilTMw+w81ULP+XbtDKHZNLZlM/bujGN63cj3p3j0\nE6cB5QjU/1AH/UWR31Q8zY2XR4gsxAHVzXXgiW4aOoI0dgWZvVce1JMSmnv2ZqXi4IjgvAAu7jXv\ndimBm09byGYhKaBuXSFF3inC71cWPE7k7XhqmAftRZ+IpFiejiAMQXNPfaFRQ2N3CNMlyKa3diKy\nbcnAY12MfH8aacsNUyqEKQi21pXd39xbz8JoeS6RMMSBaIpRK1TrEgXq/+/xuTj7g8cqehk3dYVo\nfOEMqVgGwxQVm3lIKbn92lhJ62VpS6qZXbn9arqxsjbLUxGyGYv69gD+kL7Q2UO+AvyyEOLPgKeB\nlT3JBwY1B69G1Zyet0hLpY+0b3CerTbS8PjdJDLlqUvCENQ1+kisJh2dfDx1bvof6TzQzQs0B4/4\ncrLiAnAqmiabthwvqmzL5tpLKj0vH5xJRdPc/tYYPRfaHXOIhQEnHutyXK3cDQ6GCM47ODi5PxSL\nYFDCuJLbg8cFwqfSHw4p22mkIaVk7N0ZZu8tAepfNfbuDMcvddFxspn5kfLOQpvFW+fh0U+eZm54\nqeBC4ORdC0okdZws7Q6WTVsMvTnhuP/gM706WrEFXG4Tb9BLMuJ8cjzzXD8NHcENT4BCiA0LEhIr\nKdKJzV9UGqag81QLK7NRbr9+X90pJVKqlJyBJ3v0iXkHEEJ8CbgMtAohJoDPAG4AKeXngK8CnwDu\nAXHg5/d8kKn0g9djmDmbzOzhEdBbaaTRc66N4bfKC1+lLZm6tYBhqqYYSHKrNOp7PfhMr/6eabaM\n6TYq+8YLUWjCtJ6liVWsTHlxpm1Jxq/OOj6msTNE+8m96yJ6MERwtS/l+m3V3B7ytmeuzKGaHNez\n1SW05ekoc0NLhUht/vM49s40De0BoguJbYlgaUnqmny43Ca9ua5fw1ccV1YL2FkbigJ/8yNh5wiy\nUPZtTd26eGMrDDzRza3XSp02DFNw7FLXjjb0yKYtlbNV4XMjDBC5tCVpS3rOtxFs8vP9r9wuy1de\nvL9CsKWu7AJJs3WklD+9wXYJ/NIeDWfncUqby3cWPQRstpFGy7EGEtE0U7fmCx7BeeysjZ1VUd/W\nYw3El5P4G3x0DjbjPWCNhjS1gS/kxVPnVk1XihHQ2BlUfvU5pJTEwkkSK0mWZ6JV61OcyOcY7xU7\nIoKFEF8APgXMSSkvbvkA1QTr+qWybHZjc3W3+1CL4DybXUKbvbvoKHJtWzI3EsYX8iAMNtWtbT3L\nU5FCL3ppS+Yd0hryGKYgHc+UTMTR9R7FeaRq3qDZGvXtAS58ZIDJG/PEwgm8QQ8959po6Aju6PME\nmnwV0198IQ8XP3qS5ZkI0pI0dIXw+FzMj4RxSljO+5rmRbBt2Yxfm2NuaAk7axNsqePYo50EW8pT\naTRHDKe0Ob9PdZ07JHN+Xgi/NRDjd78QYCJaXv8hhKDvYjtdp1v4/lduIR3m0Gzaormnnv6HK3t6\nazSbQQi1knj9GyPYtsTO2hguA4/PVfClB/WZu/XqqMpZFxtoCsGB6Ni7U5HgPwJ+H/jith6dz9dd\n7yMpJSTWLe1msmsRgMoH3NYwapHNLKFl0pU8lFUbze6zraqT2Lr/m2EKhCHUckYF7r0xAQJa+xs3\nzAu2LYlvXaGbv97rbNEmwF9fXryl2ZhAk5/TH+jf0WPals3S+CrLMxFcXhftA030Xmhn4vpcWdT5\nxGPduDwmrf2NJcfIpq2Kn490MsvS5CqhljruvjFOZD5e2DeyEOfGyyNc+MgAgaYNOkRqDi9ud5W0\nOS9k4/szrl2guBC6khCG3DJ1pdUYILOFomWNphr+eh+PvXCGpclVUrE0dQ0+GrtCJS4Ow1cmieXS\nIqthmAKZS4dbT2P3zq1YboYdEcGbMGjfmEQSLFvlABu5XK9kyrloIhZXEQGokBZhQH0oNwtklJA+\nxNXHGy2hNfWECvm6xRgug8bOIN6Ah9PP9XP3uxMqfyy3feCpHqKLcaZvV7e2GvreJKHWOiavz1fd\nr/VYQ5lPbPtAM1M3F8rHZgi6Tu++R6BmY7IZi+svDZOKZ9TSloC5oSWOXepi8JleJm7Mk45nlG/k\nxQ5C64ofl2eiTF6f0o2PYQAAIABJREFUI7GaqhgZsDIWQ29MYFe4kLItyfi1Oc5+8NhuvETNTmMY\nyroyXwCX3oE84GqWSestNQ8BTo5A6wuhhRDUNXiJr5TXAdi2JNisLxo1O4fhMgorv+uxMpZyJakg\ngPMpcoYhOP54F3ZWMvr2lCrolkoYmy7VFXYvORg5wXnSafWzEZalqoyDdWryK7bbyXcfyotjt1tN\nxJHooQ4QV1tC6xxsYe7eUmlnFqHa4OYbJjR2hnjsR84w9s40SxOr2JbN7NDS5qKxEiben2NxvLKX\naLC1jpNP9pbd7/G5OH/5OHe/O64qSIX6Ipx8ulfbox0QJm/MlzS/QCpROvr2NI/9yBke/qHKbazn\nhsNqotso51xS3dwfiC4cnkjfocYQEMq1zBYCNdl4VQF07AFSnCp530p5aB0mNlMIfexSF7e/NVa2\nItN+snnPKuw1mmrzt+EyGHy6B2/AQ12DrxA9DjT5mL27SCqeoaEjSPvJ5j0vht+zb4gQ4tOoXvX0\n97Y96MFU6kTeYN3IFUlIqSLJ6+3TADyemukQt10qLaG5PCYP/TeDDF+ZLHFuSKwmGX5rkq4zrYQn\nV1m8v0IishatW52t3pUoT75NbyWh09AZ4OwPHq9YlRxsqePRT54mGVFCy9/g1RXMB4iF0WXHq3th\nCMJTEdor2NjZtmTs3eltO4+sR5/QawRv7uJ1/TzscoFpbl+wZrMq9zcf+CgmWbnld62zUSF0Q0eQ\nc88f5/77s8TCSdw+F91nWva0wl6jcftcmG6jYt+Axs5QSQEdqNS9gafKg2N7yZ6dVXbMeN3lgkBu\niScfAbZtiMZUgUQl1whXrje9aR6JyMH6JbQ2Vx0rM0VVlzmTjfmRZSVycveVsYl3ShgCK135KtAX\n3FjUCqEbYxxYKqUSSVm16UxiNbVjWUiGKeg6o9NjaoJKNpagosEPMvfm53l3rjjaslUqnb2Nqt4a\no1ohdKgtwIUPD+zzCDVHGSGUC9HIlcmyVYneC21lAvigUHuhlYDfuVFGXZ2aYJ3s06RU0eL64FpR\nnS1VbvEhnDydltCGh5w9+eDB0qUNUxWqVM4DEjTtcaK7Zmdp7mtgbnipLJ9XyupFDK5q3pIbIAzV\nfQhURLl9oFk3TqkFTKP6hfODXhVJcj7wh9cLvhpb8RLWaPaatmONuNwG4+/PkYyk8NS56b3QXjGP\n+CCwUxZpZQbtUso/3Iljl+CqMFwh1hpuVCK/hJbfxUDlFK/urSfdXlG6hJbk7f9o7diydB5PnYvO\nU61M3pyrso+bhs6dtefS7C29F9oJT66STa99hgxT0HO+rWJHOUDlf9V7iS0nt5SPb5iCCx8eIJux\nyKYtQq11VZ9Hc0AI+Nfm6Epe7ofEz3c/2ayX8Hpsy8a2pGp3r9PNNLtEU3f9nvv725ZNbCmBMA0C\nTb4tfb53yh2iqkH7jpH3lXN6fU4vulAsJ8urh/P7u1wq1+yQkl9CW/mo5N17oqKdznbIpmwiCzEM\n08ByapgrVPtDPeHWNm6fi4c/foq5oSWWp6O4fS46Bpupbwts+NhTP9DP9W8OY2VspGUjTFUdbFs2\nCOFopB5qC1C3xYlMs8/kWyE7NjdCzdkxXdi4U2zGSziPlbEYeXuaxfsrICVuv5vjlzpp7q1c0KrR\n1Arzo2FGv686v0vUCuTpD/Rv2le+ttIhsllnAVwp6gAq3UFUyUWpZrtTw4SXLf7gj1d46fUEQsDl\nD7kYOWGyMpzdVlMMJ2zLrtgiWRiqy4yOAh8OXG6T7rNtdJ/dWlGrL+jh0qfOsDwVIRlN46/30tgZ\nxLYl4++rVt7rP4+R+RiRhfimRLbmgLC+ILmYVOrQFyXvB5v1Er712hjRpUQhZS0dz3DvjQnOPGfq\n+VlzYEjHM0xcnyM8FcEwBW0DTXSfacUwK+u3yEKckbdK3YfSWZubr4xy6VOncXk3lrgHM1O5EhJV\nBJFvnQxrAtgp1yyfL2xvoSPdISAWt/nZX57ly1+LsbJqs7xi81/+Nk1dzOLYD/kw/SaGx6DlWAO+\n4M620RQinwdcz4UPndDRPA2GIWjuraf7bCtN3cpc3XQZxJaSjhdktiW5+91x0snDu0Jz6Kj2PT+E\ndRcHBSWEXfzaL8QYvJxkIhpmKrZa2B5dShALJ8pqNmxLcv/9ynUiGs1ekU5kGH1nmrf/9jZzw2Ey\nySypWIapG/PcfHW0al3J1O0FxzRPKSXzY5W71xZTW5FgUDllll3eLKPSJCyBVM4VYn03uqxV2Xuy\nhvnK38dYXrVKsjwyWVhYlHzM+P/ZO+84Seoy/7+/VdVxenKOu7OJzeyyS845SDBxKOoZ4KeiYFbw\nkPOMJ3pnOMUTTCcqAgoKCBKEJacls3l3NkzOsXs6VPj+/qjuSR2mJ+2E7ffrta+d6a6qruqp+tan\nnu/zfB6L937Hw52P56AIDcdejdZxmmGki1Bh0dHllNTmz9lK0AxzB5FiFkYPGuzccoD1FyzLPEjN\nBwzTdoVI9l6GGSOVl/Bgb/ICwmD/wrWVyzA/CPSG2PH4/oQew5YpCfSE6G8PkFuaeMYiPJB4hsky\nJWF/evUH81SpTCCvVQCGDv5BezCORZHDkQWbo/bc1lBC28xIBB5+OMJPv+rHU99GX10PrXumRwAD\nCKGgaEpGAGdIi6yC1I1YwoM6A2MaZFimNWnHiQwzSGjMDB0Md4rL/L1mHLsQOodLlhgsOyOEJe0H\nD1eWI2mAyOmZfzGwDAuL/S83pmyyYRkWfW3JzQt8hZ6EKbKKppCVZrfEeXoVpBkZGhqEsdMe/OM3\nflgIFBYoSTNETAvMMNRtN7EYmNYuetKS5Jdn7NAyjE/bvm7a9nanXkhKQgNhcoqz6G7s49CbbYT9\nkaFuWDXrSxPmi0kpGegYpDM6HVZYk0tOSVYmojyTmFGvdrdr2Is9HMm4QRxmNhdqICQ/etL+Pack\nC4dLJWxao8Z6RRVUrJpi06oMGaaAETFTzlSAPVuoOZPL1IqVRXTW940usBZ2cVxhdXoOFXNTBCsK\neN32YAq2gB0cYYg+Xh5vTP0ZBgSPrCmfrW+E2FOnjxt8me40PUUVVK0rzXT1yjAupmGl10lOCDzZ\nLnqaB9j3YuPQ8pYpaavrJhyIcNQpi0atIqVk/9Ymuur7hpbvPNRLfmUOy06oygjhmcS0ptYWOcO0\nI4Rg9Zm17H62ntBAGKEILEtSsaqY4sVz17s1wxFAGkOxEFC0KLmLiTvbxeozaznwShOB3hACyC3z\nseTYypQFdSOZe4pFjO07jy2Gs7NgwD/coSoSsVshJ+sQB7Zlj0Mbbq+8wNny/CA3fq+bcPjwTj+q\nToWVpy0mO01LkgxHNoGeYFSMJj9PhQB3lhNfkZe3HtkXJ5ilKelt8RMcCOPJHu402N8WGCWAwRbN\nPc399LYMHHb/ygwZDjvSZNkZIfY/ZecFu7KcrD9/GcH+MHrYwJvnRnOos72XGY5wNIdKVoEHf1eS\nB2cFlhxXOa5HvK/Aw7rzlmEalu2FkKb4HfExcwxX1K1gbFc4gGyf3fUt1vltPISwp+eOAKSUfP+W\n3sMugAEsQ2JGMsUvGdJD1ZTUl6+wn+ZXnbkYIQShJAU8QoHWPV10HOzFiJ5/HYd6E0aYLUPScSC9\nauEMGeYrMbcIkFjSGOUU4clxkVOclRHAGeYMS4+rspu3RIukY+5S5SuL2HzZKopqhmcr9LBBoCeI\noSfWGqqmTFgAw1yMBI91cYgRS3KNNb1wpWntNbZJxgKlp8+iuzfxyaFpdjfTmbLqlJakcVs7eZl8\n4Axp4M1zozlVImMLIgTklvpYfmI1mnP4Rq25NPQEdmmWIWk/0EPHwV72vyKp3VSRtH032J6SPS0D\n5JX5MmkRGRYsE2mkkSHDbBEJGViGxbpzl9Ld2I+/O4gnx0XJ0nxc3mF9ZxoWdS812v7B0XSe0qUF\nLNpQltJhKF3mnkI0zeTVxImiw+ls7wjA4xZJD1UA554+s6kKwYEjK/c6w+QRQnDUKTWoDgVFs69j\nRVNwZzlZdnzVKAEMdvGDoia+3qUpsQwLaUoOvtpMdrF3aJtj0UMGe5+rp+7lpozDxEIk81wzxEj/\nYJA0+ntme5cyZADsDoa7nznE6w/sZseWA7z58D4iQYPlJ1ZTva50lAAG2PdiAz3NA0hLYhoW0pK0\n7++mYVv7tOzP3IsER/T0o7zjCWEpo/nEPrvJxgJuj9zZbZEsy9KXBY8/M7MFK+7sIyPtJMP0kJXv\n4ZhLV9LV0EdkUMeb5ya/PDvhk33ZikLCAZ22um4UVdiWOglOdMuShP0Rcoqz6G8PJE6LMCXdDX2U\n1OaTU5LpSLcgGK+Q+ghlZEe5B/Zr7HuyB0WoVGRl8uIzzB57X2igry1gi9rozF37/m5Up0L12tJR\ny0aCOr0t/oTNXlr3dlG1tgRlitHguRcJtizbvzfdSE0ib8oYQtj/VAWyPMOD5ALkua1BtCSPNLoh\n0g6cTwahCKrWlMzcB2RYkKiaQkltPlVrSiiozEk6tSWEYPEx5RxzyVGsOKkGb14Sf2EJetjkqFMW\nseS4KlRn4uvdMiWdDX3TdRgZZpNYIXUsjU4I+2efN/3ZwgVMLCI80j94ZJ5whgyHk/BgZEgAj8Qy\nJa27u+JeDwf05LOAlsRMkh88EeaGCHa7IDfb/pfjswcv/2B8e+RESGkL59iyyQa+BV4k59BE0ici\nTQVzPDuqSSIELD6mnPyKTD5whpnF4dbILfNRWJ2bUDArmkJeqQ+hCIpqcvHlJ2/GsZDlkRDiAiHE\nbiHEPiHEDQne/4gQokMI8Ub039WzsZ/TQrJCaiHAmbqqfL6j65KGJp2BQOqId6JGGhkhnGE2SCVq\nLdOKa5zh9jmTWmkqqpiWIs/ZF8Fetz2QxQYuRQGvBxQB/X4IhZNbnMVaHw8E7GXHQ1Pt7S5AzjjJ\ng5XgQcHlhAvPmpl84JySLI597xpKlxbMyPYzHHlIS9J5sJftT+xn2z/raNnTGTcwli4tsPOGR+oe\nReD0aBSMMEgvXpyfcMBVVEFhTXLvyfmMEEIFbgEuBFYD7xdCrE6w6F1Syg3Rf786rDs5naQqpF6g\nM39SSm7/cz9nX97E+z/VxrlXNHHDdzoJDKYWw8NCOJgRwhlmBXe2K6moVR0qqmO0JHW4NQprcuPG\n8Vizl/lfGCcEOBK0dRQC3O7R7Y0tmTgaHNHtCG+6UV7vwvSyLcxX+fKn8nE5ba0P4PUIli12cu3H\n8jj/DC9ud3onjBDg84E2TiDFiBhTzsfJkCGGlJI9z9ez/5UmBjoG8XcFaXirjW3/rBslhDWnyrrz\nllJSm4/mVNFcKqXLClh7zlJMw6LzYC+dh3pxZmk4vY5RA6WiKhTW5JJdtDDHAeA4YJ+Ucr+UMgLc\nCVw2y/s0cyQrpJZywRZF3/uQn1t/308gKAmGJLoOT70Q5Cvf6hx33TN8OXzjWHF4hLBl4Tu0n9y9\nO1HCc7NwWhg6Qs90NTxcON12oCKRqK1cXZzQtWfJ5gqKa+2AhqIKVE2hclUxFSuLpmWfZrcwTlWS\npzAows7jjSW66gZYMCrx1bTsZWDYQm28lAhVsaPNC7Bo4t0X+ti0zsXfHwvQ229x0rFuTj3eg6YK\nbvp8ARvXBrjtD/20tKe+OUgJa1Y4WbvSza//1J/Uk9nlXXjTjY7+XvL27ERqGj0r12G6E0+puzta\n0YJBBssrsRxpFnJmSEl/e4C+Vn9co4uwP0L7/m7KVwwPek6PgyXHVrLk2Mqh11r3dnHojVaEYq8X\nO29F9FE/u8hL5ZoScksXdAvlSqBhxO+NwPEJlnuPEOI0YA/weSllQ4Jl5j6pCqkXaMvm2/7QT2iM\nH3xEh9e3hWlo0qmuHKe5QNRC7esE2fekm+ZA/7QXy/kaDrLqN7eghoMgFIRpcvDi99ByylnT+jkA\nSjiMp70Vw+cjnF+Y1jqurg6W/fn35NbtRgD9NbXUXf4hBssqx103w9RYemwlhxwqHQd6kICi2AK4\nbEXiv52iKtRuqmDR0WXoEROHW5vW4NvsimArhWAFW/DG3neM2NWhTnJKfC7YyDziVGJ4gWFZkrvv\n9/P7ewbo6bNYUevgwrO8aNEnLkURXHq+jz/cM5DW9l56LcK/XVfAw08EaGpNLJpLl6U34MwXqh99\ngOrHH8JS7ClWYVns/sBVdK87ZmgZd2c7q377c9xd7UhFRUjJwXe8e0YG9yON7sb+pI4OXfV9o0Tw\nWPzdQerfbEVaEjnm+Tb2e6AniDfPvZAFcLo8APxJShkWQnwC+B0QdwILIT4OfBygpqr48O5husQK\nqb2e4XFdSrt98wK0wbMsSWd34gCOwyE41GSMK4Ih3kt4OoWwGgqy9hc/RAuNdiRa/Pd7GCwpp2/F\nqil/hhIOIVWVyicfpfqfDyEVW2j7qxex68PXoGdHj8U00cIhDLdnqGeAGhzk6J98F20wgBI9R3IO\n1rH+p9/jta98i0hutEHDeDoiw6QYErUbyjAiJg6XllZag6IpuLTpT16YZRFs2VNWY/O6Ep18MYE7\nnldwouXGsgCnyb7/814eeDQwFCF4e1eEa2/s5MffKOK4jcPRzGSCNhGf/rcO2rsSL59T4iW3zDe1\nnZ5NTBPF0LGcLhCCwtdfpurxf6AYBgrDOegrb7+VjqOPpePYE+lbsoJ1t3wfx0B/dPC0I02L/34P\noYJCelYfPUsHM7/xdwdp2dVBX3sg6TLjdQJq29eVNNcshpRExfTCengbQxNQPeL3quhrQ0gpu0b8\n+ivg+4k2JKW8DbgNYPPG5XNXURqmXROiKnb0fwHO8sVQFEFRgZJQCEd0SX2TwaNPDXLiJjfZvtTX\nzEgLtelsqlH0xisIK/6+oeoRqrb8Y0oiOG/3Dpb89Q7cXR1D93llxN87+9B+1tz6I9743I0seuQ+\nKp7dgjBNTKeThnMvpvm0cyjZ+jxqJDIkgMEuLxCGQflzW2g98TSW/PVP5O98G4CeVevY/673px1l\nzpAeiqrg9Mx+Wdrs+wQHgsP2ZRL7bBzZGW4kU30ikxIiM9Q2bRbp6DK572F/3OxfOCz54a293PmL\nsqHXKstU6g6l55fc2JJcMBsO5mVETYmEqb3vbkpeeQHFMolkZSNVBVdfLyJB5EhYFiWvv0TR9jcI\nlJahhkOjBk+wB/fqxx5MLoKlxN3VgTAMgiVlR0wXw3TorO9l/8tNKQWsogpKxim+jATHP6elJRns\nCyGlnJfnbppsBZYLIWqxxe/7gCtHLiCEKJdStkR/vRTYeXh3cQROh10XEhubjSkEKMyFK35HctWV\nOfzkl33xKRER+PEve3E67K5a11+bz2Xnpw5UJPISnqoQdvV2oya5z7q7uxK+ng7ZB+tY9dtbUPUR\n2x4zFiuWhaerg5V/uI38XduHllWCBose/hsg8TUcHL2NKKphkLN/D6UvPYMj4B+6H+TveJujD+3n\n1eu/henNeIsvNGZfBEtp26Epip0HbFrgcSUumEt3e7oev37sYgnOzQT9qbBzbwSHQxDR44XEvoM6\npmnR2GLicgo+8aFcvvqfXVMKhgsBQmVGcslmmlW/uYWcA/tQDfuJwTWQ2i82dgapkTC+5sakU6zu\n7sRFKVmN9az8/a04+3pAKJguF3ve91F6V66d9DEsFCxLcuCV5nEFcF55NoXVic8zy7Toaw+k3bGw\n81Avfa1+Vp62CG9ucgu1+YqU0hBCXAs8AqjAb6SU24UQ3wRekVLeD3xGCHEpYADdwEdmZWezs+xx\nf2TKWzhiOwJliONQo86df/Oz/1CEVcsd7NyrE9HlqMC3aUIwej3dfEsvq1c4WV6bumYhJoTB4AFC\nU26q4a9ahOFyoY0phrOEQv+iJZPaJkDtA3cnFK9jkUDBjrdQxtzk1EiEmscepOnkMzFVDdUc/eBs\nCQVhmGiDgVEBEUVaqOEQpS89S/OZ5096/zPMTWZfBMewLLvwDeyB0JFm0dXI1IeYZ/BgCFyW7RgR\ne9+Sdu7YAiQ/T0k6A+h0wkUfbCEwKDFMSY5PYcMaB6+9rU86Zc7lgquuEdz7rDGv+tJ7WxrJOVg3\nJIAnimKayAQPZhIIlFfFva4F/Kz73x+ghUJDr6mRMKv+739543M3EiyrmNR+LBQGe4JJiy4BSpbm\nU1STR3axFyEERthAD5u4shxYpuTAq810NfSl3MZYpCmJDOrs2HKAYy45atw0i/mIlPIh4KExr/37\niJ+/Cnz1cO/XKFzO0QIY7J9dTrvKawGnNEyGF14N8aVvdKIbEtO0b49KtM472Vdl6JJ7HvRzw7Xj\nW1jaXsLAkv6oEJ58wVz3qnVEcvJQujtHCVHpcNB49kUT2pYwDLKa6ql94M9kH9yf3jqmiaVqcSIY\nQOg6XeuPoerpx2DM21JV8Xa0jkqviKHqOrn792RE8AJk7ojgkZhRIesdE6kZK0Biotey7Pci+nBF\ncDhizw+patQuZ+EOqmuPclKQp9DcZo4Stg7N7hQ9Mn+sq8eiq8fC6QBflkJ3b/rfi6LYxRfvv8zH\ne050smjF9E2hHQ6ymhuRU6wqtRQVIa1RA6XlcFJ//qVxy5ZsfR6RaCA2DSqfeox9V3x4Svsy3xGq\nkvRBTAhYtKEcVVMwdJO6lxrpbfEPFVComkAPpZ7OcGU7CfsjiVssm5LeFj8FVfNrJmPB4Ewx0xeL\nCGcA7EZH//79rlHpD+m4epkWtHdObMrvDF9OVAgH2fekZ3JCWFV567obWHLvHRS9/RrCsvBXLaLu\n3VcSLC1PezNlzz/J4gfvRQmHEFKm1eDG0jT8lTVkNyUxOxGCYGkFO666jqN+fxtK9IuUikLriadT\n+eSjibcLhAumx5Irw9SIBHXCgzpunxOHa+oSdm6KYLCv8j7dNr1V1dFRXYimPRgwGEy+DcnUcszm\nCUIIfvbdYq65voN+vy3OTBNyskXSSuKIDgP+9AVwQZ7g3Rf5OPd0L8sWOyHMtE6hzTTujlZyDuxF\nGGnkjkb/HzvoyuiLQoIVbewSzs1n/7uvZKB2Wdx2vG3NqAnuVopl4W1rnvAxLDS8uS40l0pkrMm/\nsBuxqNFK4D3P1jPQORh1frD/OlYaae2KIlBUBcuIP8+lJQn2h4C5eb5myBCj7qAel/+bDh634MRN\nE0/5OcOXw+YhC7XJCWEjy8eeD32cPZaFkBZSnZjUKHz7NWrv//O46Q+xb0VGrVM712+m7j0fYMUf\nf0X+nu0oI8Z7CUhFUPTGVjo2ncDLX/8vfI2HbJFevYhld9+OkqCgD+x7bMtJZ0zoGDJML6ZhUfdS\nIz3NAyiqwDIlRTW51G6umNKM3twVwTEcjtHtL6W0UxtCoeSd5I5AaiodPHB7Oa+8Faaj02T1Cic3\nfLeLjq7kQlc37OeLVPnBqgpOh+CH3yhm3crRDUmmcwptJql+5D6qnngEYVkIyxyqv0yGVDWEaYxa\nLvazGh1ULYeT3VdeRfe6jUkjWv7KGkzn1rgiEUtR8FfWTPGo5j9CCFacXMPOLQeQUmKZEkVTUDVl\nyP832B/G3zUY11M+HYJ9yXNLpSVp2NZOT/MAy06sxp2V8Xo+rER0cCuJr51M84JRKOrE3d4UBXKz\nFd5xzuQKuabNS1hRkJPoyVX9yANp5f8K7Nm5nuWr2f3hTwx5tu/5wNWsufWHZNcfGFpOAFo4zPK7\nf4ert5uWk89EDYUwvFlIRcV0ubCEiCt8BhioWjQUxc7Zv4fKJx/F3d1FX+1Sms44n3DhHLUQXEDs\nf9kWwNKSmNH7QVdDH4pmW65Nlrktgh1a4mkzhQWd3jBZFEVw3IbhJ//ltQ72H9JTptclEsCKAtUV\nKg5NsOYoJx+5IoeaFN6T0zKFNkNkH6qjasujcXnAY4XwyGFPmEbce2Nv1aoeoeK5LXSvP4ZkdGw6\ngUWP3I+i66MKLaSm0Xz6uRM8koWJr8DDxotX0HGol9BAhKwCD4XVuUNR4OBA2E6BGMf+bFJI255t\n+z/3s/HiFQsyP3jOEo7Y43vMHjN2fYTCdpAjwxBLFznIyVYIjkn/EQIWV2u0dZhIKQlH7MxATYOL\nzvJy3VV5eKdgQTWTXsJge65XPPUo2Q2HCJRV0Hz6eQyW2w+/yQqNE6FYJgU732LVr39Gyyln0b3m\naEyXC4d/IGGwQzFNah6+n+rHHkRqGsKyiOTkcuAd76b05edhjPg2nU4aoulupS88zZL77kLRIwjA\n09ZMyasv8da11zNYEV8XkmF60MMG3U0DccEQy5R0HOhh0dFlKJP0EJ7bItjpTJ435nQkryJ2aMOF\nF4YZHViTKMFYF7kFlDcciUjCuuRfL/ex5bnghKfSHA7BzTcWsXxJ+tGx4Sk0OyI8G7g72/G0txAq\nLBl6ai996VmUBBGFsWdVqshwsvdc0YFaCYfIbjiI6XDi7uqg7KVnUCIROjccy/arP8PSe+8gq6UJ\nBIQKitj7Lx8mVFQy8QNcoGguLWkjDE+2a1JR4LSR9jRbT/MAhdW5M/c5GeLxD9pjtaZFLdIyBXGJ\nEELw3a8Wcu2/dWCa0g6iuwROB/zgpiLKSlSefTnEgN/i2KNdaTXLSJdp9RK2LPL27sTV3QVIltx/\nN8IwUCyLrKZ6it94hV0f/iQ9q9YRKizG15x+E0MB5O/dSc6hOto3nUD9eZfi6utNvry0UAwLosER\nd1cHS//6JxpPP5eqpx5FmCZCSizNQdumE+lZuRYlHGbJfXeNilArloUIh1h67x95+9rrJ/e9ZBiX\nyKCOUETSe4EeMXBpk5vNm9siOJUycTjsfGHThPCIwdPltPOHh2x3hD3Q+gfjw55ul738kINEtPvQ\nPI1EDPgtvvvTHrY8O4iUUFqictX7s/nLgwHaO820p9R0XXLPQ+lVFY/lkqWSHz054dWmhBIJs/L2\nW8nbuxMpFIS0GKhezM4PX0NO3Z60CiomigSCxaWs/9G3yW48ZL8mxKgCjuyGg9EIl0D3ejl0wWW0\nnXwmwjAoev1GiWK+AAAgAElEQVRlcvftQg2F8NfU0rnh2OFORRmG8OS48BV6h3KCZwLLsAj2Z2y5\nZgXdyKS1pcGGNS7++pty/voPPwfqdVavcHLpeVnk5qgAnHuad8Y+ezq8hF3dnaz7+X/Z9mOGgTJm\ntk2xLLAirPztz3nrMzdw6MLLWHn7baMEZ7JajZGokQglr7xA+6YTSFgRm2QbQkrUcBj/oiW88fmb\nKHzrVYRp0r1uI4Fo6lr2oTqkqsZ6JI3aVs7BOls/ZDzgZwSXz5l8/BdiSgVyc1sEx5JWx0aDhbBT\nIkR0Os3ptMWraY4WwLFlwfYe9o+wSHM6bAEsxPAyigK+LLv70DxDSsknr2+n7qA+dE9pajH59Z8G\n+Nl3isjLVfjCNzuobxg/0mJZsGN3hJu+30VersJl52fZxXCziBIOU/j2azj7+xhYVEv/khVDDy4r\n/vhr8ne+PWpgyzmwj2N+8B9ooZmzxcvdu9OOBER/H9tsY/h3iXMwwLJ778Db3Ejhrm04BvpQTTs/\nufiNrSz++1+oP/8yGs+ZmIXQkcBRp9RQ93IT3U39Se9rqlPFMqxJC2VXJid4cmgL331nrlBcqPLx\nD87ObMVUvYRX/fYWXD1d4wYkFNNg/U9vZudHP0Xdu6+k9oE/oxg6wrToXXYU/vJKKp/dktLmUpgm\nuXV76K9dRu6+3QkLnBPth7BMXL3d9KxeT+O5F8e9LzVH0uRsOdbuL8O0YBoWA50BhBCULMmn40DP\nKF95RRVUrCxawIVx4YgtVCGxEB75v9djO0Uka5msqqN/dyVItYj9rqnzzlXi9W1hDjUacUGVUFjy\ni9/3c+v3S9DD6V2kQsCeAzrb9+ioCtzzYIDPXp3LFZdmj7+yNAE5rf7BvvoDrL31R2BZdqtjzcFg\nWSVNp53D0r/9KWHulwAcgcQ5YdPFSAGcDgKoePHpoZ9H/W9Z1DxyH67uTlpPOp1A1aJp3NP5jepQ\nWXFyDV2NfdS92JiwuYY7y0FwIDJpEexwqeMvlGEYpwM8I9KeZNSHPSOGZ51nXw7yv7/ro77JoLxE\n5f99MIdTjvPgcgqUKVhEjhbCOvufSu+acXe04m1rSWustIuPdZbf/Tu23vR92jedgKu3G9PjxfBm\ngZToufnUPPZ3O6qccCMKUtXY876PcfT//CfOgT6ElEPPz1aCRhmx9QKV1fGvY3erq73/btRwKO49\nS1XpXHdMRgRPMx0HezjwSrNdExJ9csmryKG3xc4NjgngilVTK0qc2yIYbCHsdo2/nBCJo8ZjUVVw\nO5NPW0hAKMQ5ac9x9u7Xhyom4947oNPYYtDdO/4xxbpWxwq0TQvMsOTHt/Vy1sleiguTD3wz0ove\nNFnzq/9BCw1b4SmRML6mQxz1x18mrOQ9XExmyEuZe2xZlL30DCWvvUTX2g3sufKqzPTaCAoqc2j0\nOQkNhJEjtJaiCqrWlrLnufqE6wkBKAKZpLhOqGLSRRVHJJpqC+CxM25Z3nk5izbfMUxJT69Ftk+w\n5bkg3/5xz1AdSN0hgxu+040QtmXa5Zf4uObDuTi0yQk2X9gL9E9oHUcgkNAvPRXa4CCu7k7ChcWj\nnReEoOW0c2g59WwqtjzCokfjXSSkEHSuP4ZIfgGv3PhdCt9+g7w9O5BA1/pNmC4Xa2/78aj1TE0j\nUFHFQM0SkBJFj2BpDlAUshrrWfuLH8alZgjAcLrQfdnsf9f7J3R8GVIT6A4OdxMdMW73Nvez/oLl\nqJqC5lSHfOOnwtwXwRO5eGJieWw0OFZ04dDsiDEkF8tigp85Rygv1dBUQSTBfHFZsUp7p4GqClLl\nSSmK/RUmsl5WFMHTLwZ5zzsm3ot+Kv7Bufv3JPT2VaKpBIeDRNNnM/HMP9SiWY9QuP0NSl57kfbN\nJ83AJ81PhBCsOWsJB15tpruxHyklnmwXizeVk1vio6Q2n46D8dNlq05fjKFbNLzVymAC2zRVVcgu\nnLmcygWHy5V4/BTCjhBHMhZnhwMpJXfe5+fW3/cRiQxHOiMJnMWkhMGg5K77/HT1mHzjS4WHbT+D\nKezDkqYmSAsrVdfYqBgu2PU2voZDaJEwllCQmsqhCy4bEs5Sc9C58Vg6Nx47avUdH7uWJX/7E972\nVixVo2PTCey/7F8oev1lFj94D66+XiyHk5YTT8fb1hxXXG1bsyk0nXU+jWdeOORTnGF6aNnTlXDG\nT0o7Qly9tnTaPmvu/+UM0xal40V5RzbRGBsdNC0IhiDHl3obUtot1uZhhfJJx7rxeW0bnZGHr2lw\nqEnnk9d3pNT2l57v5Qsfz+eKT7YyGIxfUEqJmaZNVXz+2OR9JkdGgMcynhCdDqEqR/x/OCe71EiE\nsueezIjgMWhOleUnVmNFG2eoIyK4izeV48lz0bK7CyNs4Mpy4iv0EPJHKKjOZeXpi9n2z/0YEQPL\nkAhVIASsOLlmWiIKRwypZicyU8KHjXse8vOz3/RNyP0nFJb844lBnn8lSCAgWbHUyWevzmPj2jRm\nW6NsLtR4YL+BJc20xvWlf/1Tyvfj7CqFIFBWiZ6TulBYahrbPvlF8ndto2D7mxhuDx2bTxyyWUtF\n34pVvP6VbyIMHamooCgUvrGV5XffPhTxVSNhyp/fAlbibnVSUTHc3owAngHCgcQe0dKShAPT+5A9\nP/56/kHITSMfFexB2DTtNApFsX82zPg+9SOJqcZwJLnt2hxHUwW//O8SvvD1DppaTVQVwmGJjPYV\nSYbbJfjKp/OoLNOI6JJzTvVw9/3+hAXbpx7vSXt/pquRRl/tcpRE+VuHiXRv6TMhkrVUf7gjHEUR\nMEa4CiEoW1ZIUU0e2x/fT8gfYbA3ROehPg6+3sLqM2vZcNFyuur7GOgaxOV14CvKsocMwxolqDOk\nwDRASdL2OJMTfFiQUnLr7f2T6iRnmtDdY6/39s4In/63Dn72nSKOWZeeteVEGmm4urso2P5mYhEJ\n9NUuI6u9FaHraJEwhtOF5XCw+4P/L72DURR6Vq+nZ/X69JYfuw/acLR58YP3xqVWqLqedMZRqiqR\n3DyKXnuJmkcfwNXbTaiwmEMXvNNuopRh0mQXe/F3B+NqPBRVIad4emft5ocIBjuSOzYPLRlCDNvu\nuJx2CsR46/X7J96WZ45RVa5x923lHKjX6eox+exNHQk1vRB2pXFttUZ3r8V3ftwz7r3LNOGL3+jg\n6itzOOMkb9oFFlNtpGH4smk67Ryqnnj4sEZiJ8pk9i2VcLYUlc7MQDopDrzWTMg/nDcca5u8+5lD\nbLzkKIpr8/EVedn9TD1NOzpA2BGG4tp8ao+pyESFxyMUsS0qE5HuLJrAdvVRVXudSGTeWlPOBhEd\nevun54EjHJZ87Xvd3Py1QtaudCLSuMem20jD29poR0qTuDnsuOo6UFSK39iKp72FwdIKOo/ejOVK\nPzI9LVhW0gYdlsN2hVDHtmBWVVxdnSx65P4h8ZzV2sxRf/wVde++kv4ly6l45nG8rc34K2toOfUs\nwvmHLw1lPlO2vJC2fd2j65wEaE6FwprptRKdPyLYmWDQTeQEEcv/hWEf4LH5wWN/N8x5L4BHUlvj\nwOEQ0cEs/rgcDjjvdA/PvhziYEN6UVbDhN11Bl/5djeb1vv52XdL0i6umHQveimpfuzvVDz9eFqf\nM53MdPqDFALD5caRIN1DAnpWFs2nnZN0fW0wQPGrL+LuasdfXUvn+k3IVDl0RwhSSrob+kcVzsUw\ndItAdxBvvocdTxxAD40+99vreug61MfK0xaRXTy5drNHBKkeEtxOGBxnBkMRthVlzJ5SSnucDgzO\nO1ee2cLpgCyvYMAfP77HUrNjk6Dp0NZpcs0NHaxf5eTH3yzG6UxPCI9XCB3OL0QkEcCGNwvLY0f1\n2o4/Jb0dnSkUBcPjxRFMZKkp6DjmeEpeexFL0+wGOy4XOz56Let+8V8JoscRlvztToRlIUwTxTLJ\nObCX8hee4u1rvoi/pvbwHNM8xulxsPbcpRx8tZm+dtsFJL8yh8XHlE/7jN38mP9TleQ5wSPFaywf\nOBKxFUwyG7TYcpaMVQzM6O7PBgV5StIc3kgE/nCPP20BPBIp4c3tEf728MSqwO3IgWDZGUEsadAc\nGL/CuPqfD1L1xMOohn7YosCSVKWDqd9LFwvoXLuR/e+8AktVsaLnaOyzLU3DEfBz9P98l0V//wve\n1qZR6/vqD7D52zew+MF7qXzmCZb+5Q9s/s8bcabokHSkICVJbdIEdtpDb8sAppE4imYaFjufPoQR\nzjRwSMpYu8kYQoCaRlzF4xntzx772Zt+utWRjhCCf708B7dr9Mjo0GDz0S5+cXMJ11+bxynHuXE6\nwJclcDhSP78EQ5I3tof57V3puz/YQljj8x8LsOyMEI3+nlHv529/C8Uw4sZNKQRNc6x1fNPp52I6\nRvuFW4rCYGkZ+674MK9/7mvUn3sJuz70cbbe9P2UQQc1HELVIyiW/RSimCZqJMzyO387o8ewkPBk\nu1h1Ri3HX76G4y5fw4qTa3B6pj/QMz8iwakG3ZgIHhnh9fmSTr8MEWulvEC7FXk9Chec6eWRJwcJ\nJ84xnzS6AX97KMDlF6eZpx1lIr3ohWlQueWRuKfsmSImQDs2nUDOgX14UvSun3KUWNVwd3ey7C9/\nRCqK7WEpJVJVEaY5NO3m6eqkassjVDz9T/qXrmDnRz+NpTlY9X8/RxvhV6lFwii6zrI/386Oqz8z\nlT2bt0gpsQwLRVPw5rsZ7ImPRkop8RV4aD/Qm9pPWEo66/soW56ZukxIqlmzdNIhtCQBjZjN5Tx0\n55kNPvIv2QwMWNx1/wCaJtB1yXEb3Xz7hkKysxTWr3bx7ouy6eoxqTukU1Ko8KmvdqbsHhqOwL0P\n+fnEh9JvypGskcby3l4WPXJfknxggT/aiW2u0Hj2Rbj6eind+hyW5kCYJoHySvZ84CpW/fqn5O/Z\niaWpCMOk7YRTaTjzApQJnquerg4c/X3oOQurRXtzoB9Lzs/rdn6I4FQ3rLFNM2Ld5LQkhRsxplsZ\nzkFuuK6AYEjy1AtB2/t3GvV+RJ9cTDRdL2FtEt6SU0IIXvrmDzG9Plb/8ie4uzsTD96qRtPp51C5\n5VGURHPuKRhq+2ka+Jrq423XEhyvAFTTJGf/Pmrvu5vWE05DTZBCoUiLvN077Gpn7chJi5BS0rq3\ni6btHZi6iaIpFFTlEOwLxXkJV68rRXWoZOW5U/aht0xJeDBj85WUiD66UUYMKY+IcXWuoCiCz/6/\nPK66MoeGZoPiQpWigviAUWG+SmG+/fot3y3mU1/toLfPTOpkFwxOfGxPVAid/fQ/EUkeioS0yN2/\nh95V6yb8WTOGolD33g9Sf/6leFubiOTmEywpY/VtPyZv324U00CJBtdKX3oWw+2hf/FScvbvG4r4\ngl3PIaSV/GFxgTmo2NF/yec/FpjtXUnJA19P/Pr8EMGGMXxCpVsYlyzRQ8rJRRo0NZpeodj7E47M\nyTziAb/FP58ZpLffYsMaF//5b4U89nSQG7/XNW2foShQVKiyc2+EVcsn3m42nV70hjcr9dzdNKNn\n+TC9tgdy49kXkrtv96jWnLG/dN0730fbiachJVRvmXix3mSPSDV0Sl55gfZjjk+xFYmwrMPmnzwX\naNndSeO29iFPSVO36Krvo7AmD8u0c4CdXgcVq4rJL7dnLrKLvbh9TgZ7E+euKpqCryAzNZ8S/yBk\neRg6FwX27FoCT+84DDNxNHiyY/MRji9LSXscrq1x8Pfby3n0qUG+/l/dcV+3ELB5gwsZvbelUyQ3\nkpGF0O4/9ScfqYRA901sJvFwoWfn0Jdtz1C6errIrdsT51Ck6hEqnnmcV2/4Fmtu+wmero7oDKEk\nUFqB0z+Au2f0PVcCg6UV6NmT88yfi8QE8Oc+NsBpkfmZjjc/RDDYg64vC0jSFnksqUTzeIUbY3E5\n7SK72LZUxa48GAjMKSH88hshvvD1TiQSXQenQ7B6uYPmdnNarY8tC155M8zVX2zn9BPdfPv6wgm3\n4xyvF73UNLpWH03xG1snLByHIq4TWL7nqDVDv/cvWcG+yz/E0nvvsPvWWxam08Xeyz9E18bjAKi/\n+D3ouXnU/v0vdgQ3dtNI8TlTlfTCMgmWlSd9P1BRjeU8zFXVs4hlSZq2d8SZqlumpKuhj02XrURz\nxEfGhBCsPrOWfS810ts8MOY9cLo18isXzo1qRjBN21FHUwFh26alOxQGQ+CL2hyNTGmb6LicYUK8\n8maI397VT0OzwcplTs482S6ODoXs719RwOUU9PWbHP+ORiwLNq518ZVP57G8Nv1gR6wQ+qflHuSu\n+HFP9TpZ9N5NXHrL2Tickv1NcN+z0Ngxc0GPsbnK6VLcdBBTVUcFRGIIXachEuTAJ66jsLGe7O5O\n+opK6KmspqCxnjN/90uEZaIZBobmwNJUnrn0PWg7Xqdy9w5MTaN+3QYGikqmeniziC2AT35qC2/e\nOzcfasZj/ohgy7IHXkcauxxziIjZ8IwklgssBEOVAoaRvIxWiNECOPYagMc1ZwbuUNjii//RSTA0\nfCcKmpK3d0VmJLhiWbbx+lMvhnjw8UEuOXfi1fSpvISFrlOw6+1JtyaW2D3iRfQJPlVEwnS6aDjv\nklGvd2w+kc4Nx+JtacJyuQgWl8Y9ULWcejbdq9dT/PpWtEE/xa++iNM/MGNFfJHcPAyvj7r3fIBl\nf/49ih4Z6lwkNQd17/3gDH3y3EQPGUMRq7EoQhD2R9DyE0d0NafKylMX4e8a5ODrLfi7gghFUFid\nw6KN5RN+qDtimYybg2XZAQSnwxbRpmWP1/OwSdFcIxKx+O1dAzz1QhCPR3DJuVm84+ws/rElwM0/\n6x3yFW5uDeJ0wPvflc3zW0P09lusW+ngyRdCvLF9WPC99naYj32+nbt+UUZFWep7byQieealIJ3d\nJm9sD9FwKI/XV72TUn8LR7e+TZY+iOrWqLliM8f+7wdQXfZ07arFsLQKfnSXpKF9eq87O1fVYNkZ\nIS5eMvF8QLPDQ8cdifNGtFwnn/5kBKHoQEH0H8AAkI95zacI3vE6xu52tLXleK44mnfe9DDhx/dC\nUAdVYd2LT+L73Gn4Pn3yZA9xVtmcZ+G670m2z1MBDPNJBAPoevKiirHEDHLD+rBw1nU7v1jTolN5\nUVxOezAPJLBHSdYNJiaimRsi+PmtoYRfy0x3MA2FJHff309utkJLm8HyJQ5qKjVefj2MpglOPtZN\nlje1CUkiL+F1+/dNyYpBAOHsHNpOOBUsScWzjw/l3Cqmgak5EFLSt/QoDl78HkIJnsalphGoXpTy\nc8KFxTSecxEADWddyPHf+FLSPLipIAElFGLRg/fSdOb5vH3NF6na8gjurnYGapbQdOb5CY9hIeNw\nqklPEcuSaVUS+wq9rD1n6aSnfzNMklj+8PzsTTQnee2tEJ/6aseo2o8duyM88GiAPfsjozzjY1//\nE88GuffXZVgWnPf+poQBk8Gg5P/u7ueMkzz09FqsX+WkunL0tbV3f4SPf6WdcFgOp4UXrgYhqMuu\n4eXSTVxjPsX7HvkYrkLfKC9uIcCpweevgBe2Sepb4WAbtHVP7VocKYB/UNWB8dTrk9rO1s0FNG7t\nwooMj+uqU2HtxWUse2ZL6pU3ABvygRANv/w7rz66D8LR7RgWGBbB/36SE/N7yKmcf63be5/oYF/v\nktnejSkxv0RwsoKMVFjW6GINgS2Ax97sYjm/cYUdMrkdwBxKhfAPyqSBFEVJ3E16uti1z+DG73Vh\nmPY+GIYdPFcUgWXBN79cwNmnpr7Ax3oJD/R1DxUhTBbDm0XDuRcD0HDuO8huOACWxF+zGJmOldME\nkU4nYpq/5NipJwBncJDKJx+h+PWXeP2LX2fXR66Z1s+abyiaQvGiPDoO9SJHpEQIRZBX7sPhTv9v\nvJDErxDiAuAngAr8Skr5vTHvu4DbgU1AF3CFlPLg4d7PDNNHd6/Jp2/siCt+juiwY08kadyouc1g\nd53OZ2/qoLcv+dj1138EeHjLIJZlP2CecZKXb36lAE0V6LrFRz7XFt+YaehDBYbioPRbH8ZdnDhi\nKAS4HHD6Bolp2ZMDr9dF+MFfAiRxM0yLmAB+88v7gMlFKz3FG8mt3kXvwUY7G1NVKFy5jJB/Edvv\nTX/cqH96J2Y4/mBMw+K13/RRvKZ0Uvs3u8zfCHCM+SWCVTV9fyqHBm73cHFVKGwL3GSV80LYqRNj\nRbBuQCL9NrIpxxzg2KNdCX2BhYDjN7rYtS9CT4pBbipYlh0tGIk9INqvfe3mLlYf5aS8JPXpNrId\nZ2tn9ZTcIUyHk7ZjTxp+QVEYWLR00ttLh6zmhmndXqJTXZESR38fFc8+EZfCcSSy+JhyTMOiu7Ef\nRRVYliS3JItlx1fN9q7NCkIIFbgFOBdoBLYKIe6XUu4YsdhVQI+UcpkQ4n3AzcAVh39vM0wX9z8S\nSOoKGtHtQEgybrq5k67u1ErTsiAwODzGP/lCkD/8ZYCPXJHDTd/vTtiZdCzPPDvAhRek7vYlhEBT\n7ZjU8Udp3PFlhYjeN/7GEyLJau+LCuDJIxSF0qNXU7x2JZauo7rS66o3FivZ/UymeC/DjDO/RDAy\n/eqisa2S7dBk6urjZNsOBIfTJ2KFHHZSbJo7M/OUl2q86yIf9z0SGCp0UBVwuwVf/lQ+wZDko59r\nmxXdrhtw/yN+PvGh8dsdxoTwSzmStp+mt/2R8lsAptNFoKyC1hNPn9T+TgZnbzdrb/1xwkjw2P1L\nl2TLqpZF4bbXZ00Ep9Po5HCStT4X9wofekBH82poHo22SAAm4dZlRkz66/oJtgRBAV+Nj+zF2fOp\nlfJxwD4p5X4AIcSdwGXASBF8GfAf0Z//AvxMCCFksgTrDHOeQ016SidRt0vEBSrALp5uajUnnHkW\nDkt+e2c/e/ZHeOzp9JpN7dw9sdRBIRQcupO+z949wb0bZjqn6hVVQVEnX3icXVVGuG8AOWbKVqgq\nvvIjK5VtqoQtwdZgNh2Gg0pHhI0ePw4xueFrfolg07JzehVS5wUnM2J3OiCYSgQLu2rZPyY32DDs\nSmhn1HvYNOdkk40vX5PH2pVO7rh3gJ4+i+M2uLjqylyqyjX8ARNFFTBJf9+pIKXtJvGJD6W3vC/s\nZUlzHe1pbt9UVHZ/8jOs9tdhDQTYm7WErvXHzEjKQzLKnn8q4QOWBAJllbz5ha+x8ne/oGDHW3FC\nOVHEd7wJD/MwHttIGv3dgD3NODeZ/HUZ7DJ49ccDGCOKS/v39kLEz9Efz54vKROVwMgpiUbg+GTL\nSCkNIUQfUAgk7xCTYU6zZoWTh58YTBjk0FR414VZ/PHe+C6fiYRxuvgHJY88mX631cqKifuXC4+L\npnmecxojv7aavgON6IPBISEsVJWs4gK8RQXjrJ0hRrPu5OaOagwpCEsVlzD5c18xXy2up0Cb+Pg/\nv0QwQDg88bzgkbij647sMBcj1rFI0+L9LueBEbwQgovOyuKis0Y7NZim5BNf6cBIIIAdGixf4mDP\nfj0ti8/J0tJusmtfBFWV+AOSlnaT5bWOIeud5laDcESyqEpDUQTm3mHj7WSCUHFpHP+Hq3GesJYr\nSp1IuQpVgWAEfvkA1DUlWGmGyGppQjXjv0ABRPLzkarGoQvfSf6ubXFpHpOxgOs9avWk93WyNPq7\nWXZGiP84JkJW+2SnKKeXlg54e68gN1uyeY09+yElNHfY/1eWpFdH++YeuOa/lOg1MLyCpUO4Qedf\n+jo5bq392o9m5EjmHkKIjwMfB6ipKp7lvcmQiovOzuIXt/cRSZDydubJHh785+QaGXjc9rP91GcQ\nJWcu0pFSTuhhcnB/L7nHVVD6zhU4Ctz4d3fT9uedhBqHrQ21PBe+VUWYgzoD2zogQVrgXEDRNBaf\ndSI9dfX0N7WiKAp5tdXk1FSk/E76m1rp2lmHHgziyvFRtHo5WcVHZjdLKeHnXRUErKg1IxCWKrop\n+E13GV8qaZzwNuefCHaM0wkOEgtciPaoH7FMIoSwlaFh2P+LqIVaqrmmOc6zL4eobzISuhmZFpx+\noocff7OYq77QRmNL8paaU6Gt3eRjX2gjHM0gcTkBIVi2WGPAb9HcaqIokOUV/PsXCllamoMEul15\nFITjTbiFpnLeizeQvyG+9Wa2Bp//F2jpNujst3js9QjPbNNntIlEXnExuZqGNuZJwlQ1motLbJ/K\n7CxWerx4/QNJtjJMyjNcCN5avZbAJL0vJ4ccKjLp/Oyjs14RbEn4XU8pLw/moAhpFw4KyRV5bdzX\nV0yPqSEEZCsGVxe0styVPGJlSvhiy1J0K/G3HgzDfT/pw5XXMUNHM600AdUjfq+KvpZomUYhhAbk\nYhfIjUJKeRtwG8Dmjcvn7wB4BOD1KNz+P2V860fdvPJWGMuCgjzBdVflsaRG47mtqWduVMW+F8QQ\nAtaudHDdR/P468MBtjw3OInsv6j/MJIrcjtY+lIfelcljkJPnOhL5M4ipSTSOciiz25GjRa55he4\nyd1Uxt6vP02wrpfyD6yh5OJlyGj1nDQs6r77PIN7D+fYmD6KplF41BIKj0pv/Ozac4DOnXuR0T9O\nsKuXxudfpeK4DWQfgSkU7YaDbtPB2DukhcLeiJdBS8GrTKyScv6J4GS5eTHhG1NwyYTweMTWyx1T\n9RiOzKkc4Inw6luhpNNelgW/vXOAk4/18PP/LOETX2mjuW36Lb4syZAAhlhQXbJ994gQgwmRPskX\n/qOTipIc1uev4KjeuoSC0FRUctdVJv08IaCiUKWiUGX1IpUPXSDY3j19XfPi9ucda+k842nkmBlH\nza1w5n+v55wyW/i23RyashhXK3N4b939sNvCc9laXBeuRKipbeimjuT0SC+d33l0TkxPPu7PY2sw\nBx1lKOk6JCW/7K5gaICU0GU6+VFnFd8sPUBRkqmyfREPhkw+VihIPMq8KVzZCiwXQtRii933AVeO\nWeZ+4AIv27EAACAASURBVMPAC8B7gScy+cDzn4oyjf+9uYRQ2MKybGEM8PaucMpbodNhR4vrDhq0\ndhjRmI+k7qDBZ27qZMUSlWM3uHjh1TCGMfo2m4z8HLj4qHZWth+i0MhFEyAN2HHdYyz7+ilkrSgY\nMnS3dBOjL4yjwIPQRu9o3gmVo4SxUBVUj0LVR9fT8VAdxRctRXGq4BxuirPs309h29UPYYXnzTWb\nEMsw6dy5b0gAx5CmRdsbO/CVFc+XFK1pIyIVRJI7qECmHMeTMf9EsG7YBW6J/vixJhgw+kpNFhVO\nJpRjEeCRuJxzNhd4PAryVJyO5FNaEV1y/6MBrv90Pjd/rYgPXZduNu7MICU0tUms0vUs6zuIJhPk\n2homem8QV6Fv3O1pikKxw8EpO7YhO2bu2HquX8Urt+7B32ZHXbJK3Bz7yRXk794Ku+1lHs3XGBic\n4txiWz/hv9vpCMaWvThvyeakL69BKCBNiaLNjCC2OwLNvgAGeMxfQESOPc5Ym5TRWBKe8OfxL3mJ\nU14jlpIy8q4AJ3jHj97PBaI5vtcCj2BbpP1GSrldCPFN4BUp5f3Ar4HfCyH2Ad3YQjnDAsHtGn1d\nrFruRFMTXxsADk3w5Wvyyc9T+X9faufNHWE7BSK6/Nu7DIQwhiw4xxPAviy4479VCva00f2PLpp6\nc4fekxGTvTc+hXdZPnknVYKA3uebqP3S8XHjViqBl3VUIcKpDkWIR68IuSdU0PPU9Lr1TBTLtAi0\ndmCEw3gK83GPDayNXNYwaN+2h/76JizTwlOYR96iKoRILPmMcNh2qhjbDGyBU+4IE98D1CZfNcie\nRLBiWkTweL6U00o4Eu0CN0LASmnP5ahjxHGqp6Rkxrm6Hm2CMQYhbCE8D0XwRWd7ue2P/SQbBC0L\nAgGLxhaDT3xl7kz5+l05KAkEMIAlBI6cCeSGqxqD+Udz8PZXwJIITcEMTLdVRjYVx5VjBO1or+rQ\n2H5PM4OddTi8HvIWV5FTvZKBpsmZtgMgBNaI3G4zbNG+Y4BHvrSbYHcP0rRwZmdRun4VWaVFUz+k\nOYqdE5aI+GveQKFRd9OiO2nSnZRoOjVOe1pCSljiDCaJINjf85V5bRRrc8cOcTyklA8BD4157d9H\n/BwCLj/c+zVtODR7LI65/YTCo+fyM4xCUwXf+HIBN3yni4g+2k++pEjhv79eTH6eSluHwfbd4bj6\n3vE85mPxJKcDTtrs5rMf0CnO1tFT1NAM7uthcN9wyoLqm5iYsyImjtzETg3CoeLIm0Ld0DQQ6umj\n/tlXQFpDqR7e4kKqTtiIGONXJ6Wk/pmto5wjgp09BLvi0wBjCOyiuiMNTcAH89r4v94yItJ20BdI\nHELyr/ltk5r8n7IITtOXcvqQEvwB2/LMoQ379erGcD/6dIl9Y7oRjfJGBXCyItZ5OvVQUqTxnesL\n+PK3upIOZsdtdPHbO/sJR+bOrOiAK5um7AqqBprQ5PDIrSsaoRM3o6TTQjuGgOwNpay//RJkNL87\n3OKn4dbXCeyc3jQJzePGCIXZ/9izmBEdaZogBD11hyjftI6cmgr665tH7JvAmePD4XUTaO1MfcdJ\n9J5lMdgxfAyRgQCNL75G9cmbF2zV8WJHkN2RRK2648soVSxadQffbF+EisRCUKpFqHKEeGUwBx1B\nnmIQsLDTK7Cn1lQknylqZLU7/Qr4DDOMyzm6jb0Q4NNsR5+M12pSTj3ewx0/L+XOv/k52KhTU6nx\njrO9rFvlGoq49vRZaJpI+x6Ql6OQ7VM4YZOLj16RQ2mxhvS3gK6jP/8avU90pJ06FWkL4FmUO/6C\ngLQk3U/Wo/oc5J3oRhmTCiZ1i8Ce7rS2NRNIy6LhuVew9NEPzoPtXXTu2k/x6mWjX+/sJtzvj7NO\nS3ofUAS+8lKUI1AEAxyXNUChpvPQQAGthpMaR5iLsrupdk4uXXU6IsHp+FJOL3Z3htGvpcqJTOYE\nEcOh2dEES5Kweiy2jXkYBY5x5sleigu7ae9M3FBDNyRvbI+PAsTweQX+wcMvkB9YdhGX7X2Qcn8L\npqKiWSYN5cv55D1j0xxTI4RAOOxBI+b56qnOYelNJ7Pn+i2EGqZ3urv97d0YofCoHHVpSlpe3cby\nd5xF3uIqeg82IQ2D7KoysitKQQg6d+yla8+BKbf3k6ZF+7Y9LD7jhGk4mrnHe/M6+UGHZ1RKhAML\nE4E1RghLBH2WholC7LbUoLto0F1Dy/VaDjQsah2DBKXKMmeQC3O6KZ1HEeAjgpECGIZ/9rjt4EiG\npCyqcnD9tfkp3teSdh0dixB2HvHXPjf8kG0L4MiEBTCAMTAx56Xm32/DUWgXyUmXGBrTrYhJ8GDv\ntAc2JkKgowsrwRcpLYveA/VxIjjU3Ye00niAEwKhKDizsyjbuGa6dndestQV4jpX8/gLpsF0iOB0\nfClnHtNKLHbTLZBzaHaqhWnaQlhTR6dbSGwRnOUBNRqBDofnVNe48bCsxHlhDs0+tNIilYMN8ULf\n5WRWBDBAWHNz96r3UGT0sro4zOZLFvOZD9fgy1InbLeTCMWpUvrelRz60VYAhENB6lOfWh1obk0o\nZIWAwY4ufOUlQ1FaaVkYoTCq00HxmhWE/YP4m1qnvA/h3n4s01yQEYNaZ4gvFTXw575iDkbceBSL\nM309rHUF+L+eMtoMJxLIUiz8loJJovzh0RgIfKrFjUWzm0uYIQlaivN4xgtDFz4et8JV78/hV3f0\nEwqnHu9dTsHF58bPxJivbpuwAAYm5r4kIPe4cnqebmDvjU9T+ZF1ZK0qxAqbdD1xkJY/7ZzYZ08z\nVkRPlnmIlSCQprldCFVFJgvARXFkeSk/Zi2ewrwjriBuJjlshXHT6jkphD3oWdHObTECg3ZWfmyZ\niThEjFwuMGgrP1c0T8kwbLEbS7cQdi4KHrftKxycq40DRnPGSR7+9o9AfLBbwMnHeqipcPDmjs64\nATAyB+yRO7Vcnu+x+MjOvXT+qpNup0rNJzaCOrXBQAhB9poi8k+touKDa3EUeLBCBu0P1dF6187J\nW+OlzGiQQ/937z1I1646pLSQlsSdnxs3vTfOAcQ2Gv85lsW+B5+gdOMacqsrJrL384IlrhDXl8QL\n1k8VNfOD9mqCUiUkBVbaTsyCg5HZzSXMkIKMgcWM85ErsikqUPjlHf10dJpUlqkUFKjs2K0P3Rdc\nTsG7Lspiw5rJd08bS88LTXhXFMQVuiUKdAghWHTdZqquPprBvT00/2E7g3VzxxLNU5if9Fz1FMR3\nTc2uLKXtzZ3juwZJC29R8kh+hskxHSI4HV/K6fOc9LjtDPyYwDVNu61xrDiufwAcTnCotkBNVwSP\n7RQRjgw3x1AVyBopgBn+2emwl0t3HmkW+fgHc9nyXJABvzUUwPa4Be+92EdlmUZlmcY1/5rDz3/X\nh6YJpIRgrHvWrN5/JD6nyfdrXsRqKaa3xZ72LL5gKe6abJRUEaI0EC6V6muOQXXZl4PqdVBy8TKc\n+W7qf/7apLaZVVaMv7kt7nVpSbxRo/PeAw1RC5zhp5JQd7QYInkx9ygUVUEikEk6nViGSetr23Bl\n+3Dn5Uz4OOYbUsJPOqroszTkhNuQQJ46f1OeFjzJOobG6kJGIgChzItxeS4hhOCS83xcct6w646U\nkrd2RPjnM4OoquC8072sXpG4kM2aZEOp7ifrKb5wKa7SLBSXGt2WYd971XinCKEItCwn2etLyFpZ\nSN23niOwa/ZSIEbi8HrIramgt7559PknBEWrlsUtr2ga1adspvH5V7EMMz43OIonP15AZ5g60yGC\n0/GlnB5czuHWxbGLQlXtFIVYq2OJHbo0leGo8FhGRoiltAVwsqkIVRkdXY7bFvY0XWTuD7aF+Sp3\n31bGXff5eealIHm5Cldcms0pxw1Hvz743hwuu9DHG9vC7NoX4Xd3989yoFty+fo23qe/Rc/AolHv\n7PvGM9R8ahO5m8tAEZOaIpJSomiq7TU5AtWtkX9qNc13bMfonXjCfen6lQQ7u0cNauL/s/feYZJk\n5Znve05EpM+sLO9tezvdMz3eMIZGM4MQoF0QYkFa2BUsco8uEkbiCiGDNALpSlpdOYQQXDlkEIuH\ngZke2z0z3TPdM+1dee+yKn1mRJxz/4iMtBFpqrJMVsXveeqpqswwJ6syT3zxne97X4Gi9Zb9EFIN\nffkBcO7AyjsPU1QQSkFtkrbUZpQRVhkWbwyj49jhil/HZodz4GbSgYtxNxyUoU1MlhkA63+nzHY2\nwvCYd+MaaiyKoJenRWPafJ9d9q2y3NW4dKIktU1SrpnVus0IIQS3HLDjliKZXx4YAed8ZaUQ0KTT\nrv36CTQ9tgMND3SDqxwLTw/Dc6AJ9Xd1mY+NEggOEV0fPIyrHz9R8XnXitajB5AIRRBbyMpQE2Dq\ntfPoe+geCLbc7ntngx87H38IkblFTL96HkoimTOXE0FA417jv6ualBG4OYLQ5AwESYR/oBfezlar\nZKJMVh0Em+lSrnpkRtjthYGobnVM8+76VaZlifOzwdlmGkDpCdJhz5zHCIKacpPz+wR8+P11+PD7\nzTtxvW6K++90YmhU3tBewIY6jo/eM4jbxq5hIlQ4AahhGUOfewnUKWLvn74ZNgMnomyMlta4ykEk\n4/IDJqtwdPsQXqpcNk5yOTFw/H4EhsYQnVuA6HSiYUcPHPXa350zBrVE1oQ67GBlGLRwxsCTDM7G\n+txJNws5Eq34NWx21JSF5pWEG0lOIICDAwXVvxm00NgvKLjXtYwXI3WIcgoKQOEEP+ZZxO3O2tAD\n3jboSQ5kuX3qvRuUasmL7BtJPQDOdge1SQC4Zv1nUXV4YGRFzXD5sLiK2a9fw+zXr2UeVDl8R9qM\n9YCzcPZvrixpIhhGfCnPWp5xKLEEFm8Mo3n/roJ9CKXwtDah75F7MXPuIkKTswA47D4v2o7sh91X\nqDOsJJIYfvok1EQyI68WCCIyM4/22w6uxUvbclSlJthIl3JNMItvONec5PKTseEo4HIU6v5mB0K6\ni4RZRk40MM7IP7fJUnSt098jwSYRKEphkH/bYRuO3eLAl74ahFxBb2A5bkMA8MmfA95Rdx6B70+U\nnFhZTMHQE6ew53MPF/UbNgqQi/1rqU2AvLByeSzBbkPT3h3A3h1Gg4Fgk6AWaaxk8QQEuw2qomo3\neCX+cLGFgNZgYfBeFp3Oise/2Xk+UofLcTeSqbBXSf3zVZM0eoeQwDv9CwgzTf3h7b4FDCUdiHKK\nflsc7grtNi3WAY/L2LgoGsuUq+kQZALgnMeJpi1vBcFVh4enVt4MVwaLz46i9b/sBRUpSBEToM3m\nDheZnktLcWbDGUNwfMowCNYR7TZ03nlUS25wXrSxefHaUK4KEQCuqgiOT6JhZy/sRQw6LDRqq6XW\nLONKSKFYOiEZ2YPlUCbbaxT1OIoIdZudk6ea8rZghk3nnmMONPgF5H8GHXaCj/ysHx96Xx2+9sV2\n2G3lLbtIIvCnv92ExnpacEy7DXjsQTt+45f9eOk7nfivj4oVyTLHhpYRODmOit1fi5RRqHEFicmw\n4XOrhasqXC2NRYN2AFATSYh2CY17d8DZ6C8etRNi+nRocgYz56+sfMCbkGci/nQAXEju+0AAwwKT\n8HeLbfiXpRb8zkwv/maxHb22OA46olYAvBmxm8zLunFRweMlLmfUWh6uNVhcxdWPn0DgpQmwpArO\necEczzlH5NrmqAdOQ4npXE2p8ftUTcoITc0iMrsAzphW5lZC2Sc0YaxCxBlHeGbzGF9tZmrLNjke\nB1zOwvIGWc59IzgcgD0v+2u2rk+ItqxmRjJZqE2pE1ybAGmzIAgEX/zjFvzW5xfw2vkEKCXweSk+\n8Yt+HD2olYl0ton4vU804NOfX0QyyU2NmygFPv/pJtx3pxNf+7t2/Pu3wnjq+SjcLoL/+lYvjr8p\nt5Sh0gITZ18d6o61r7guuKBMgnOE31gbi+X4UhCjz7+iZQrKeKFqQoantQnN+3Zi5NmXzJ2EOIer\nuQnJSATJ/PcmY1gaHIW/p3PLZAcSzOxzq3UV6k5CnUICU6oNcZ57QTkT8yI2R/HzTZOw09opadry\nUKrN88VUUozm7FJNcDVUtmaRQVmKY+RPTmOqxYX9f/ljhmoRrh31ZTcTrwe+zjbMX7xeMBwiUNT1\nFdY4L1wbwvyl6yk3OQ5Qiq67bi2pBkFMPiOEkG3pKLcSaisIlhVtGczpyASliaRmdKFjs2kBcH4w\nZOYupqtKGEGytsnfZwtngLNpbhTwl0+0IBhiiMYYWpoE0LyMysP3uXD3MQeePRnDF/4piOlZBSrT\nqkREAXjkASc++Qv18Hm1D6XXTfHB9/jwwfcYqxXw8BR4MgGWSJa9xNbyjt2gJrW9xSCEGGaPWULF\n/A+GKj5eKTjnGH/prKFepCkEiAeCsNd50XX3rRh66iQUgzp2IlC4muqhxIxLOLjKEJyYRvMWCYKP\nOkN4OlwPxTAbTGAnKj7QMA0BHF9cbDfc5mLSjSfmevCplhGIVqJw45FELQAGzFc9ipWgJZPaNSA/\nUbJC1QILc3RzjJUqQlRK81sNyspSCC4JotcGJbg5/s+Sy4nmg7sxd/GaVqvLteY2R70P9QM9ALTS\niOh8ALGFABauDWrlD+kbORXjJ89gx6MPFjTRZePv79bOYRDDeDta1+KlbTlqKwgGtEBYLpKBNcva\n6hhpB+tNFnlyJvC6c5UodNc43a1Ob8rjbMt71/u8FD6veZDpdFA8+rAbjz7sxrWbSYxMKOjtFLF7\nR2We8HoAPP/ZJyuqMXN2+0zvikvCOJjK0vszWcXiU8MIX5xf2fGKkFgOlWyIy4crKmYuXMHshato\n3NMPb2cbAjeGC7YjhKKutxPLY9Vx0tnsPOYN4OWoD8sspdudh8wJ5hUJbqoW0QommFVseDXmxZ0u\nqyluw8lf6ctHv2GNm3yGYgktG5hdLhFPWEFwldHnaeXUWVz8Ty+Atb+xtjW7iq70qdHN1ZvTsLMP\n7pYmLI9OgskyPO0tcLc2gRCCyOw8Jl4+B3AOZqJMxTlHcHwqHTQbUT/Qg/DUHGKLS1ovCNHK+1pv\n2QfJaWmel0PtBcHFEAXzGksjhQgdh137YkzTHGZMm0RJXl2PXmdMU40WdltGhoelssOWLiV277BV\nHPwCmcxCpQEwAMSGl2Hv9lZmNKFDCZKzUQRfmwZLqFg6NYHYoEnJwSphiqpln4uMxXDZVtXMgBeu\nDJrqSEpeF9REsuiFwtPeUvGYNyteQcVnWkfw2dkezKsS8j/8IuHokhJoFOSiPYUJTnEu5rGC4I2m\nlN53ug8jVnyejSe0r3K7cC0qQp+nMwHw+iDWOUxL15KLMXBl81177T4PWg7uznlMicUxfuqsuTxm\nCq4ywxW/bAil6L7vGKJziwhPz0GwifB1d8Cm+xpYlKS2GuOKQVOGFuXUhOrb6EGu/kWplv2l1LjL\nWMdhzwTJegE8JRlHOYsVo756YUX7zfyfayu2OyaEwN7iQsN93RA9NojeygP4cnH4fabNe3afFz33\n3Q5Hg980q20WAANAIhDE4FMvIh5YNt0mGYpUNuBNjldQ8XMNU7CRwka4BkHBPnsUbZKMo84QzG49\nCDhcdHN1l1uYEC0RAGdjBcBrhq4IsZ4IbslE4YcgeHb1NvPrxdLIRFnvTSIKcBg4zBVsRwjcLY1o\nPbwXTXt3WgFwhWydILiYwoMRRgGu/pjTUbzA3kg2TbdSXqV7mcXKiI8FMfjEKSRmI2AJBVxlFSlF\nEIFC8NnQ+OY+9H/sLvR/4q41+XRQUUDLoT0FQS4RBLQe3Q9XUwP6HrwLnXceBRVXsFBTIkAITUxV\nfsxNzg57HD/fOIFmIQkBHAI4Djsi+HjzWFoQ4H82TONN7gCMPtgS4bjfbX7jYLFO2IrM4Xop2hYv\nO7MwJz4eNEwCqHEF0Wubxza5FEo0VjSZAQAgBJLTCU9b8/oMahuzdcohilkkG9UBGz0GaI+Jgrac\nZlZfXMw4Q78LUxRNls3qSC6LajRZhM/P4dJHfgCpyQmucDS/bQda364tRZWjGkFSAvuCU4T3cAv8\nd3dh6cXxFY/HjPqBHtg8bixcvQk5GoPDX4emvTtyVBtsHnfpiXIFrCiwrgEOOqL4/bYhRDiFjXBQ\ncJyNeXEx7oJHUHGfaxnvq59DklOcitblhMKdYhwNll3yxkKglZoZunLywgZoi41jHZvhspn5+jXU\n3doGYs8kEDjj4El1TebptcLZ1IDl8Slwk1pgIlD4utrRcmiv5fq2DmydK6Le3FZusFuKRFKblPXg\nWs8qRuOa+oRZMKGfSxQ1u+VQxFqWK4EeAFfDdQgA5HmtcdHW4AKXWYElcjkIDhGND/eu2eTqbmmE\nu6XR9HnRaQcRaFUDYSIIqOs1tyCtdQgBPIQhzgj+YK4Xs4qEBBdAwfFUuB73OpdxOupLBcCZOWFE\nduJ3Znrx223Dll7wRlFK4zdZYdAlpno3VNXKHleJas/TFUGB5FQEQ39yGj0fOQpqE0EoQWI6jKE/\nfnnTmWUUw9vZivlL1yGr8ZzYgIoi+o/fZzW0rTNbJwiOJwud4cwC4GzrZKOgWZevCke1yVQUMnrE\nLNWc4TGwY85vogO02mKrM9mUtZpYqUOE/66OFQXAOsUcitaawI3hymTUSkAECn9fJ1zNDVU75mbl\ne6FGTMs2yKl6FgYCxgmeifph1DnLQBBhAp4N+/G4b3GdR2sBoHoav5QWuswp6raRtFwrNjIAbnnn\nbrS+czeoTQBLMsx+4yqWXp4CSyiQ51bu6LlREErRdGAX5i/fTNnZc7iaG9OKDmpSxtLIOGILAdjc\nbvgHuq063zVk6wTBuntbKYkdoHjZBABk66wqSqEmpZqaVB2OjKB7sdIKaxWvKGthuyl4JUPbyrLH\nFFew+Nxo1cZTKUvDE5XvRCkIJZpmZFaGwdncgNZDe+HwG+sybzVORn3pALhcZFCcjnmsIHgjMSpB\n47yyMgg9AM4+hihox7XKKVbFWtkjew41o/mxAYg+O5ZfncbCk0NQI5qdfNu796Hl7bsgOLRQhUoC\nWn9yL6hNwNS/XK7qONYDpjKMvXga8UAwLWkGEPg622D3epCMRDFy4hSYqmrzOCEIDI6g886jVn3w\nGrF1GuMA7Y4/GAZCYS2QrSQG4jyzfzn7KSoQjmiWzImkcclDMSMOizVFXohXJJmT3USnxhXER4MI\nPDu2FkMrbzwrKINoPrAL7uZG5L+B44tLkEtI7WwlGF9JHR3HmOzAxyb7cTluZV02hERS0/llLCOH\nFouXv5Kml64ZNS2bWTBbbCit79qLgU/ejbrbO+DZ14T2d+3F3j95M8Q6O4hEcwJgHcEhovnHd4Gs\nYpVvowgMjiAeWM7Io3EOcI6Z1y9DiScwc/YS1KScMb/gHFxlmDz9+pr0iFhstSBYR5K0LG2l18J8\n++VyKVavlpQrP942o9pNFoLHht5fPgbBKZatEKE7x6lRGZP/fBHXf/O5DdWd9HS0VlzL7m5pRGRm\nvuAmjqsMc+evVnF0m5vbnCEIMPvfmb0ftIxMgNnwZ/OdOB31rNHoLIqSTGqJiOWQ9r2S+ZOQTWOb\nuxWp9jwtNTrR9pN7IDi0+l4AoHYRYp0Nbe/eC1uT0/x6zLj2fI2xPDRu6O4GAMHxKUTmFox35EBs\ncW2067c7Wy8Idjm0pS+jJrlSMK41UzgdgNejKT2YNcAJNGXOkTI3iMRS9cKpL84r07TchvDACHgy\nUd0lNkqw+/ffBP9dHSACrai7lhAC6hTR9bOH0PG+A6D2jck0MFWF5LRXtA+hFOHJWdP3fDK8tfSB\ni/E23wJ8ggqJ6J89DhtheMgdgIMwlIqUFFD881KrJexSa6iqeeLDWpFbMbo7HICqlkL4bm01LFmj\nogD/XZ2QlxKmeulEpFCWaq+8hZnEAxxce840aWNNRmvF1qkJBrTAVypicqE3r+U3saWWJMCYFvwC\nKUtkmpFL0++CdVMOmnUcXb4nGEotyUErl5AkbRmOsUyznQUALQCuVpMFESn8d3fCfaAR9lYPbM0u\nUGllASwhBBAImh4dgHtPA679+rOrGlslcM6RCIYx+co5yNFYRasSnDHEl0OmLkSEbr37XTO8gorf\nbh3Gs+E6vBH3wEtVPORZwj5HFHOKDRcSpbO8CU6xqIpoEmvnc0sIaQDwrwD6AAwDeDfnvEBAlRCi\nAjif+nWUc/4T6zXGNUWXUtONjPTHAK2sYi2RJO16oapbaq7ngRFwzit38aSA72gb3PsaoSwlEHh+\nDMpybtDKFW46x3GVg8UUBF4YR/29XTkJCZZQsPTSJNRo7a2y+jpbsXhzpLDRk3EwWYGzqQGxeaO+\nBAJnGcYZFpWztYJgQcjYGOej1/zqgauYta3eVOdMLa/kqzw47FqphNORyQxn157pgW5Szki1+Ty5\nx2FcqyG25NLAw1NVa7IQvDbseeJBSA0OUFv55Q+loJIAR7cPnoNNCF+Yr8oxixFbCGDy9BuQK61l\nz8Isa7IdcVGGx3wBPObLjQGVMmukGAcctSeX9kkAT3HOnyCEfDL1+ycMtotxzo+s79DWGEnU5md9\nztUzbqqqJSj0TLA+n9tSSkKyAsRXoeeerUahJ1g43xLSmDysGetUGgBTh4Bdv/sA7O0eCE4JLKGg\n4737MfT5lxE8O5PebvnMFLp/rvBtyDkHoQSeA00Y+9uzoA4BdcfawWQVVBKw/NoMRv/6tdW/wA2g\nYfcAguNTUOKFfUSBG8Owez0gogAwrtUAEy2B0X7s0LZKZKwnWysI5tx8OYzzQpkcQchkgIFMeYMR\nHrdx0wWQarywZ+rX3M7CbSm0Uo1I7Um6bGa6P3wEthZ3uqasmuLi1CbAvathzYNgORrD6AtnSnrJ\nF4NQCoffh/DUrKEIu9VUoXGHM4TBpBNJXuyCwtEhJeCpvSD47QAeTP38FQDPwDgI3lpIorEqUFIu\nUOsZuAAAIABJREFUzAB73Bmre31fcRV67vlzvf7d5dwSsmwrsbFv/6n9cHT50vKU1K6FGX2/egfO\nf/C74EltflJDSYx94Sy6P3QERBJy5nCp3oEdn7oHN37nRQz/8SsQ/Q7Y29xIzESgBGq3yVe029D/\nyH2YeOV1RGdzrytcZUiEImg7egByNIrY/BIkjwv1O3pg91o9CmvF1rq1UBRzlQajon5VLb9m1ywA\n1tH9WSk1rkcmxLy+2KJiqEOEe28D/Hd1pifPasOSKuR1qDsLDI6C89UFXNQmwdfVbprRynaj287c\n7Q6iTUxCMm2cAwACuWiQvGlp5ZzrvtjTAFpNtnMQQs4QQl4ihLzD7GCEkA+ltjszN7+JbaWzM8A6\nhGjZ3uzHJSk3ANa307etFEqKzPVC5Y3ZW4SGB3uM9dk54DuS+5ZcfGYUg597yXDeonYRHe87AABQ\nluKIXFmo6QBYR7BJpgkPrqqIzi2iae9OdN93DG1H9lsBcBacAzInVV1k2XpRWSSq1ezqncIEWilD\nOZ2t+fVkOuVkF/U3dalNCbZ1jfuq7ZEpQdcHDqPxkb61v4XjwNKpFej1VkhiObxqe20mK5CcDvi6\nOxAcn8zpQCYCRcuhvasd5pZAIhy/0jSG35zuh8w1RQgjppXNKalFCPkRgDaDpz6V/QvnnBNCzN5U\nvZzzCULIAICnCSHnOec38zfinH8BwBcA4NjRXZt31irWAyIIGZ13s5U+QrSMcMVz0tad7FczTxPJ\nZGImALUVPifVOcBkFYKBOZGzP68OlgL193aj8XgfqCQg8OIYFn44XFOOcQBAJfPQq9hz2xXGgW8G\nG/GjcD0SnKKOKvjJunnc4w6u+thb76+tMk1aR0z50JeT7dUzAWLq7tWsgc4MXdBdEDLHMIKxWp0T\nq0K269DF//QCqDw72fEzB9HwcG9Zyg2c8xWXR3DOMfmPF8Dia9/k4vB7NWmcVZQsiCkd1LZbD8Dm\ndWPxxjDUZBIOnxfNh/ak9IMtAOCVqC9VDmH+3nCSzVkKwTl/s9lzhJAZQkg753yKENIOYNbkGBOp\n74OEkGcAHAVQEATXPIRo5QqANj/r3fdGLqEruQk1Ox6QUQiqQVbrDhc8N4O6OzpA83oUiEARujAH\n6hDh2dcIpjCEL81DDsRNr4tKMHclru9X74TvllYITi10cfT60PhIP6598kRNBcL1/d2Izi0WZIQJ\npajr7dygUW1e/jHQgpdidekytiUm4R+XWsEB3LvKQHjrBcE6+S5vZlAKeN3az/mBb6kAKnuSczoz\nJRHZz+nHBNa+Q3kTo8vsKKfOpgLgyiE2iua39KdrzIqej3GoMRmCU9KaC0z+l2aBMkuoq3KcqwT/\nQA8CN0dWpaanJBKYfOV1NB3Yhcbd/Wjc3V+9AW4hzsXc+Npyc1FHOQkMD3kKRBVqgW8C+FkAT6S+\nfyN/A0JIPYAo5zxBCGkCcC+Az63rKKuNmdNc9u+lHONWujIVi2v1v0DeXF+bvR/VsEee/IcL8B5q\nAexCWqVHjSuY/cZ11B1rR9cHD4MpXMuTc46hP34ZakwBzdILTu/zzevp3z37m3ICYAAQ7CJsrS40\nHu/D3Ldr5z7O3daMup4OLI9OpJ3hCCFo2r8TDqt0LYegKuBktA5K3pyd5BT/udyMe1zBitVws6nJ\nwreq4spThKjkr6nXkxGSqTXL/gK0zLSsaI0XBg1L2wE9AJ7/7JMrDoABQKyzl5Vc4Zxj+fQULv/y\nDzHxDxfA1RUEs5wjMRmufL8VIDkd6L7/Dti8bhBKQSiF6HSACOXLvHGVITg+heGnT2ryahYFvB5z\n428WO4oEwBwUHIedYbzNZyJav7l5AsBxQsh1AG9O/Q5CyDFCyBdT2+wDcIYQ8jqAEwCe4Jxf2pDR\nVotEUmuC05ucjbKzevNyJJbZTs/WxuKZcrZKkRUgHNW+6/JoNT7Xr1a5JzkTxZX/60eY//4gYqPL\nCL4+g+E/fhmh12fQ+YHDmiGGW4LgliB6bBj4xN0Y+qOXkZyPQo3JUCMyWFLF4okRzH9/MH1c3+1t\nhiuAgl1E/b3dK369GwEhBG1HD6D3wbvRtH8Xmg/sQv/x+9C4u7qW1FuBSdkGyaSyK8QEJFbkEJph\n62aCy0HXAq70NsJskjUikdjWrnEr1pk0oJg4erY0mhJMYunkOJSlBIKnJ9GZaq4wwjALrDAk52MI\nX1x7aTQdZ30dBo7fn7Y3poKAG989UfFxmKxg5vXLcDU1QLBJ8HS0QrBqzAAA/77cXLThjYLjFxon\ncIuzNrv6OecLAB4xePwMgP+Z+vkkgEPrPLS1JxbXMr2SaO4WSqAFqsuhVLkcUs3Uqzy3qmrGSBZp\n5MU4Jr58Puexvl+707BhjhDAs68Rlz7yA7h2N0CqsyNyPQBlKXfllCdZxtAqDybX5k2Ho85rZX5L\n0CAqptKWEuGwmbY+lId1dVwJlWaLSz1PyJZ0luPhqaoZYgAAlxnmvnsTzY/vyPGT50pKTzFVgybV\n2dH9kVsBgSDw7BiSgTjsTa7yzsE5ItcWMfxHL696vCtBcjrSP/sHerA0NGpqs2lGeGoW4alUOehr\n59F29AD8fbWVKak2nJdudnNTVrMBsAVSreMKUMxNV79ZLlYuR4imOKHfPMoyEEvUbI1vpVTbHlnH\n3uIyVPKhdhG2Zm1+jl4zMorQCLwwhpa37QTJa6BT4woWfjhc1bFWCzkaw+L1YcQWApA8LjTs6oez\nvm6jh1VTtIgyeqU4hpIOqFmreLoL6GrFobZfOQQlWgMbULyJTafcic9sOzP3IEI0FQufR6tJrvMC\nts3Zkb5aqmm1uXxmCoQQcM7TXxBIgVGE4BDR+b6DAIDEWLAiE43g2ekCd6ONoOXQHjTv3wXBvor3\nBQemX7uIidNvYPbCVcSXVt9NW4sQArhp8WxRmG2MTbZFFdED4fzPu968XAoCbT6WxEyCQpIyfSNb\nmDWxsc8ifHHeMGPLVYbo4FLJ/eNjIUx/7QpYQgFXWKrvQ0Hw3AwCL45VfbyrJb4cwtCPXkBgcBTx\npSBC49MYfe5lLI9ObvTQao5fbJxAvy0OG2FwEhUiGG5zhvCOutWv1m6fTLDeKZztKqeoxTO1lQTA\nZscxy/B6XIUak047wC2L5Xzq7uhA06P9EL22HBH2Uoh+Bw595ccrWu4khKDjvQfQ9HAfxr/8BoJn\nplc46tVDCEHDrn407OoHZwxTZ84jOD5VekcDQmPaxBu4OQJ/fw9aD28/ybTjngC+HWwsaLDQ8ZYI\nki1qhGhM6/WQxMxcn0iW1/xmsxVqwus/26QtW9pWTRt7IxzdXjBFBaGksBmZEjQ/PoDFp0dKHmfm\na9ew/MoU/Pd1g9oolk9PIXJpc9bvT5+9CJZXG85VhplzF+HtbAWtoOdju+MRGD7ZMoZpWcKiKqFD\nSsAvVGe+3j5BsNuVqf/VP3+iYB7A6s0T8aTm9AaYb1eM7I5hHUEwF1l32MsLggWq1b6lLBa15pC1\nWcZaCXqX8Wrp+tARNDzQk+4IriSjSyiB6LFVvh8hsLd70P/ROzD4uZcQOmeoNLWuEErRccctsNf7\nMHfx2op1hbnKsDQ0Cm9nK1yN9VUe5ebmce8i5mQJL8b05cjM589GGB71bs6LqcUKiMYyDcuVlBMV\n0xLeSkGwmLoGqQx8ebxqNvZGdP73Q2h6S792zaKkoA+DEAJnnx+Obi/iY6GSx4uPhTD9L5u7l5Op\nDPGAeXY7HliGq6lhHUe0NWiTZLRJ1f0Mbo9yCIEaN8DpvxstnSWSWpdvsZKJbMtl842087icWslD\nnVerNzMLysrxBxcEzf5TX7ITqJZFzqon3UjyZXZWiqPbi8YHe3IkcVaq+6uXUFQCtYvo/NnN1UPU\nuKsfXXffmusAl8pckXLKe5BSkhhZexOQzQYlwAcaZ/DplmG0CjIEMNjAIILhAfcS3uwpvSRrUUNw\nXlkADGTUJYwQBM1MaaOx2wBf6lri81TmdkeJto/LpV0vPC6guWfN3Ey9t7Sg8bgma0lFWnT+rrvL\nQB9XIKi7vR1Njw3Ava92tM61l2n+WknedV6OxjB7/gpGnnkJk2fOI75c+mbAojpsj0xwqcCSscw2\numSOrGjlE7rphk62kQaQqS/Ozyhznuk89rlzl9iEIuMpp0HOaTcO6G2SpkaxTvq2RhTqAa+889V3\na5thJ3DR86f+L0aT7UoCaEfXxnXuqrKM4NgUkuEoHH5fegnN09oMT2szOOeIzi1CSSTgrPdDVRSM\nPH2yrGOzSoODLYSLMlDCUxkAbb3c3DvOYksiCNo8zFiunFlCNu/N0Ffq1qhxrCwc9lxXU72JDygv\nS627qebYSdtBj94OfKt0OUKlNB7vz2liLka++o+93YNdv3s/qF0EESm4yhGfCOHGbz8PFt3cJYOE\nUnhamxCeLkwCEUGA3e8DU1VQQUB8KYjR517W5mTOEQssITQxhfZjh+HrNDKHtKgm2yMILnbBZ0zL\n+NLUZVAPQiktDIAB41ox/efsDALjQDSe8a832tYocC4nCC5WSyQIANvYCWI1hhjZsKS6LgF9scB5\nVSrcq0CbGF/Rmv9UFUQQMPP6JQg2CXIkBiJQSG436ge6Udfbma4va7/9FkydeaNkmY4qK+CMFWQk\ntjqMA380140FVQLPCnufjfjRLMh42Gtlg7c0BIDbnZuIYBwIRzLzr15TbFj+Bm2ONdIVFlLXDM41\nRYm1mLqyA2AdQrTSuFJBMKWGZXiEUqCru7xG8QoRXGVmqRmwdHI856GBX78bYp09Z45y9vjQ9T9u\nweifv1rNYa4JrUcPIH7ilDbXqioIpeAAHH4frn/zR+CcweZ2gwO5tcNcW62bfu0CvO0t226OXm+2\nRxDMmJaVNcrq6h3DjCNn1hJopqnCCLOASVU1OR1dgkcoUmNm9JgoaucuFrib1jFjS8n4LL88ic6f\nqawcYSXZ3mKlElxe/4wp5xwTL50Fy5Jx4qoKrmo6wNrvDMlgCDOvX0bgxjB6H7obgiShrrsd3o5W\nRGfnoSRlBG4MIREMF1yQIzNzmDj9BrruPLKeL23DuZ50IsSEnAAY0NyHvh9usILgrY7TWVgaR6Gt\n+oVT8njFejIItOblfPRGPED7rDkdmjFHuc6l5VBsBZHAuP8kZxtiek3jnGA6uhNAdestl1+egHtP\nQ0E2WG+O4ynL6vGvnIfnQDMaj/dBcIgIX1mA1OgsCACpTUD9vV0Y/YvXNnTFsxwkpwMDb3kAwYkp\nxBeWILpdCE1MIzq3kHYjTYYj5gfgHPGlIJwN/nUa8fZkewTBgDYhOR2Z+qnssgcjVLay9VHGMxOf\nJGlfxdQjjBBFQE1q2WmjwDaRNLYJBd94p6IqNMOlD7UYx/jfvY6uDx7OkUFbaV1wUQwuDpxxBF5Y\nf+mdRDAMpdwlV86RDEcx9NSLkNwuJINhiA47Gnf3o66nA97OVtz87jM5AbW+X3hiGstjU6jrbq/+\ni9ikLCrmmamQanVrb3kkk9U9PVmhz7VJObOKp8O5luTID75sUu5x9V3cTiAYql5GuFTQVyoBoqqm\n1zR5IQY1Wv2mv4UTo2h+fAdIizut6qMmFCjBBJRAAonZCOa+fQONx/vR8dMH0v0f7j0NptdMQolW\nHpHc/GouVBTg7+0CersQWwhg4crNdABcCo41utZZ5LB9gmBAC3pj8dJ3zICWPVZV80yuEZxr23s9\nmTvzlbyJKdWaF/R9lZQjkT7mRFI7T7YEEOeZTMYGkS2zs5pa4GwWfjSM0IU51N/fDdEjwdnvh2d/\nU1UnB01sfQhNb+kHESiISKHGFaihJCb/4WLVzlMuXGXppE25KNE4lKjmsKQmkph67SISwTAa9+4o\nDICzmHr1PLztzaBr1Biz2eixxcFMIoEOaeO1oS3WkKJymEgpSaQ+dbF4qqE6S1KTMS2Zko9RiYJ+\nTGmFihKiqI1HUTMlcroGcn4gz3n554gnChIoLKFg/O9er3yMZcCTKq5+4hk0//gONNzfA84YFp7W\n7JD1VTbnDj/q7+3KyRYTgZquziWmIjURAOcTXwpVtFJLBRF2v28NR2QBbLcgWKfcN2Ikpsmj6QFC\nOe5v4OUpPBQjPwMhCloXbyhr6SQaS9UtC4XNHetMvhpEtWR2qENE4yO9qLuzA2o4ifknhxC5ugjP\nvqYVZemzJ1WucvCkCiJSzH33Jqb+6SLmnxxC0/F+SI1OhM7PIvDcGFhi/f+uDr8Xq23T4qqKxevD\nqN/RCyqKpoEwIZrDnK+7Y1XnqxU6pST22KK4mnBBznMf+i9VEF632MQY9WHoEBSWoIWjmUBYZcZ1\nwID5dYEUec4MgWrNa9nHlZWMLXM0lmnY1l+LrGhBezkkkgBj4JIAgEAemcPIP9ysikW8a2c9On/m\nIFy7G8CiCuZ+MIiZr10BiyuY+Y+rmPmPq4b71R1rN7FTztUU5oyDJVWM/925VY91I5BcDk0n2ajC\njgCECunaYRCCzjtvsTLB68D2DILLhXMtECZEu/t2FtEL1lnpm1YP0BgrbHwjJBPwZge7jAHJzdHl\nX+0AWHBJ2PP5hyDWOyDYtbep90grABhab+pkB7r5EwhXGAb/8CWEL8zBe7gFglNE6MJ82qM+MRnG\nxFdy/e43AkIpWo/ux/RrFyq2TM4/TjywjPpdfVi4fMN4Iw6oyc3daV1tfqFpEv+x1ITno37InKBF\nTOI9dXPY77Ask7c8sXhh05teGmeEykrLrMlyxmQjn0prgt2uwiSKJGrZZr1EKhLTssQpnd+K+0Bk\nBZi9mZ6zw1WYs127G7Drd+4HlbRrF60T0PL2XXD212HoiZe0x1wiPHsaoSYURK4sAHqCWylSesiB\nxGwY1CEiNryMqX+5hOj1wKrHuxG4W5tARLEgYUUEAa237ANnDLHFJdg8bvj7uiA67Bs00u2FFQSX\ng77cJAiVaTJWcnzOtUxvMXtOSgGsQ2aSUm3SFQRAVVLZg9ITbbkBsK3VBeoQNWH0vOPa2txo/6l9\nqLu9A9Qu5AS8gl0sqfVLCNGciQyaSIhAER8LgssMwVc3zgmuHOq6O2Bzu7BwbQhyOAIqSYgtVDb5\nc3BQm4SmvTuweH0Y3OCCzMHhat5eou0S4fjp+jm8xz8HFYBoJVu2D7ICRKJaSQBNraIlEqtz6Ywn\ntbIHIFd7XlYqNOowqFfWj2mz5UqzMQ6wyq8FK161owS+W9tgb3UhNhJE+EKW9BcFdnzqHhAxz7re\nLsJ3uAWOHh+8h5vR8d4DYAoHIQCTGYb+8BQiVxexdHIC7T+933i8CsPUP19C4IVxw+drCUIpeh+4\nA2MnX4USS2iZbsbQuLsf/r4uAED9QM8Gj3L7YQXBlVBJfbARZgGcqmbqebM1i/PRn9OD5rVAFLXl\nNiBjxGGzaeMzWw4sE3u7B/0fuxP2NrdWjqByjH3hLJZOTkDw2ND53w+h4U092tLQKv7O1ETqhysM\n/js7MPedmys+9nribPCjef9OJJbDiC8tIx5YBi9HQi8FFQQ4G/yQozFT6T3Rbofd56nWkGsKQqwJ\ncFuiqNXtn9ATGA5bphE6kay8FriYJnqFeulGrDQAtrW6set3H4Dg1PV6GZKzUVz/9HNQwzJ8t7ZD\ncEvGczYlaHy4N2OYkUpuCgB2/Oa9uPih7yMxFUZ0cAmuAb/hMYKbwLGzWtg8bgwcvx+J5RBUWYbD\n74MgrUFizaJsrGtAJTAGcAPnuWwDDaPndFRVy0IQok2Wej1XdnAZTwBuoXC5jnPN5Ud/WM8EKKpm\nl1ytmNidt1So/+xy5NYk60NLmWMAAJEofEfbILglhC/MITmXudAQG8Wu33sAos+WI3vT84u3QQnL\n6PnIUdiaXEVLHbThrOJiQAmIVBsKAExRMH7qLGKLAS27zcrUkM6i4w6tpiw6u2C63Fisac7CYltC\nUwYUei+ILGuyl8USD5xr28RW0Vxp1tehGy9VgZXYIw984i5IfnvW6poAe4cH3f/rKIb/6BXUHWsz\nnZcJJfAcbDY0zCAiRe9Hb8fij4Yx+IensPfzj0BwS6AiBWcMXGaY+P8uQA1voDnJGkAIgcNqeNs0\nWEFwJSQSGS3IfJTU0pdNypXbUhQtI6Cy3C5fMwksIyOMbHcgHQpAkLSJ2m7TAtSVZIcp1ZYGxRJZ\nbn27eGaSz3aHG7nSj0Nfujs9TiIQLDw9gvG/1ZoY/Hd2psob8nQfJQFdHzgE0WcvGQCXC1OYJqOT\nfzyVIfjqVFXOsdbMvH4ZsYWAdjFY4TFmz19F30N3g4qitvRmsM12UYWwsCgLQjRL+mxlH0kCBBEI\nhdf23IxpAbeU1Ritz+mrCa5Xgb3dA3ubu6C8jEoC6o61g9go1EgSnHHD+Zszbtj0lj7G0TZ49jRC\nWU7g2q+fgP/OTniPtkJejGH+e4OI3qjN+l+L2sG6AlaCmuUmlC1NFollsrmxeEbCJlvephgEgGTT\nMhD5yhDpbQy0LbO/Ox2ZDmIjKM0E8LKSasCj2oRvdHyj8+tSQLF4OgCe/+yTmIrvxsEv3g3Bmbus\n0/BgD6LXF7H4zCjsbW5Qu0E2gBLYW42fWwlqQtGyEkJeUxznWHxhXKtDXmc4Y2CyAmrLXTKMzM5j\n4eog5EgM9nofmvbuhKPOC6YyBMemKip9MCIZjiA6vwhPezP4a4XPE0rTtWgWFhbIzHH5q2EUWnAq\nV19LN4doHLAxbRyUaEmUWKLiVSAzWIWWz4JbAlfNb8OpTcDiiVE0P7YDJG8O55xj4svn4ez3w97q\nLqgZTp/DJYHYKNrfexAjf/IKZr95vaIxWlisBisIrhRZAZZDmYytUZ2sWZMFSQW5uspDUtYmO7st\n8/xKsrm6eoUZut989u+61nAl5QX6+FPZYOXUWUwsDaDhQWN5LcEhovltu7D4zCji4yGwuFJgo8kZ\ngxxMwNZIV2UPyTlHYjaK0LkZNDzQXZB94AqDPL++3f+cMcxevIalwTGAMxBBRNO+Hajf0YuloTHM\nnr8Knnr/yNEYItNz6L73GOw+D7hJ/pfQlMtSGW8TrjLEA0G4mxvReecRTLx8Nv04EQQ46n1o2F0d\nNQ8Liy2B2YoYIdpzax0EA1p5W7K6JQDZZWuVlELERpdNrxHyQhxqWIYaljH5TxfR8b6DADJz1PyT\nQ5j//iBsbW7U39sJarQ6l4KKAvx3dWCEIq0aUYvEFpcwe+Eq4ktBCJKEhl19qN/Ra0mdbWKsIHil\nVNoklp91FUUtGNV/11nNh8XtApBSstADcUEwFnPPDoorgfMC+07RazO9yxe92nmWT09CjRzSSiKy\n9ucyw9RXL6P7545AcOQeo5jcWcGwZIahPziF9vfuL8hIA9rSm/dwC6b/7Urx11dFps9dQnBsMi1z\nxpmMuYvXwBnD/OWb6QBYh6sM0+cuof+ReyHabFDihUug5boNAQAVaFpmx9PWjB2PPojQ+DTUZBLO\npnq4mhqsydnCIhtmoiXMK6/JXzN07WJWXq0wD4xoQelnn6xYwtJ7uAWEZmyOgSy93i9m9HrnvnMT\nSy9Pwn9nB4hIsXxmGokJbdUtOR3B9U89h67/cRjulMa70bxDqJ6B39x2yGZEFwIYe+F0er5XFBVz\nF68hsRxG+23aDQLnPJWEoNbcu0mwguD1Il+bstIPQDHrZT1Y1LPBoqgF6Ypa3G9+JRACHp7NsUcO\nX1nQlszyYk+msrSUDlc4rv3GM+j9pWNw72sEOCAH4hj7m7MInZ+Ds9eH5sd3AirXpL1EqjWylfF3\n4pyDCAR9H7sD9lZPzoSd3oZxOPvqcOgrP47ItUVM/fNFxIaWV//3MEFNJBEcnSwoaeCqFgCbOcIl\ng2Eo8TiaD+3B1Kvny5KmM4UQeDta07+Kdhvqd1gSPBYWpiSS5qtqK3F+qya6Vn1+03Q4ahqg87DW\nA2EUABMbRftP7Ufjm/tA7QIiVxcx8eU30vOivdOD/o/eUViqxoGR/30GwbMzOQ/L8zFT5Z3YyDKu\nf/p5ULuAg196q2GjXPTGYsaxrwbRVvYK5/vg2CQa9w4gODqJxevDYKoKwSahad9O+Pu7rWB4g7GC\n4PVAN7tYLfmlEtnlE/kBtiBkSjbMlvf0idPQ8tPguJyDJ+NALJxjjxy9HkDkygI8+5vSZQica40S\n1Clq9XQMkBfjuPHbL6RqwARIDQ50f+gIdvxmvbaPygFwsLgC6rWVzv6mgl29BtjR4TXfhyCdIfYd\naYVnfxNufPp5RG+uTeNFMhwBodSwrpcztej7YezF19D30F2Yv3wD8gqknAilEGwSuu65zVQuzsLC\nwgBV1fo6nI6svg8A0ejayVKWg9tlXqrhcQFB86Y99dULho/v+L/vhXtnA6hdmyO8B5ux6/fehGuf\nPIH4WEir8zVIonDG4Nrhx/LLkxW/DJZQMfH3b6DzA4dBbZoOPFcYmKxi7G/Xxrp5vYgHjJMqnHOM\nPvcK1EQyfT1QE0nMnr8Czhgadvat4ygt8qlymtCiauRrAWc3ayiKVu6QlLVA1izI1b/MJu/sDmSj\nAFvfN/XFk3FgeshQZ3L0r17LkeHSg1Pv4RY0P7Yj59BqVIbgFLHrdx6Ae1dDelsqUlC7CNFj4r5U\nMHxS9Hed/MwwoQSCQ0Tnzx4seY6VIrqc5o1thEK0m7sByZEoFq4MQqm0I5wQiE4nuu65DTsee9CS\n4dlmEELeRQi5SAhhhJBjRbZ7lBBylRBygxDyyfUcY02QlLW+j2hMk7QMhjbUlh6SWLxWWU96lKDh\n4V7s+9/HcejLP47dTzwI1876dACsQyWKtp/aB0BThjAqc6OSJpG2UhZ+NIzB3z+J4NlpxMaCWHhm\nFFc/9jRig0srPuZmQDBbQeAcSixusip4o6QBlMXaYmWC1xpJ1L6KlTNko38gODfOFur1xHqWopzl\ncjMN4+wg2DSDmto3HAGWJyC/cDo3AKYE3R8+goY39RhOmIJDRPNbdxYsk7W8czeow3jiJgL3c/0S\nAAAgAElEQVRdl4nBtXvtnNIkpwOu5gZE5xZy6niJQFE/0AtfTweGn3rRcF/OOcJTMwU1w8UglKJh\ndz8ad/dbsmfblwsAfhLA35htQAgRAPwFgOMAxgGcJoR8k3N+aX2GWENsFg1tM0tmHQ7D53VzDJZI\nwvPuN6HxTbekyxAET73hoYhA4d7TCACIXF2AZ38jqC13PlHjCiJXF1f2WlKEL84jfHF+VcfYbPgH\nerB4fagiq3uuMqiJpGWRvIFYmeC1xOPWaoH1SSw745qVYc35XafYpEeIJp+jfy8nYCwnI1yMrLvc\n7Axwx387gIY39YBKgmkm1tbkBMnzpvUeaCpa7lCtOinOueZNbwCLru1FruOOI3C1NIFQqmn1Ugpf\ndweaD+yCo84LW53XcD9CCaitssZFzjnqejutAHgbwzm/zDm/WmKzOwDc4JwPcs6TAL4K4O1rPzqL\nlVNificoaNTOdodbPhOB46Fbc+pw02VkBighrd9j/gdDYEmWk8HkKgNPqlh8emRlL2UL07R3B9wt\nTYYlJMWglmPchmIFwWuF3aY1pWXr+eo/y7ImMxaOaktvippZbssuYyiH9SiqJwSw2wFfc87DtjY3\nWt62E7SECxsHUH9fd85jarSyJhOjzHBZ2WIOsHhhRlVNKJj7wWBFY6gUQRLRfc9tGPixB9B9723Y\n+diDaL/1YFoKrnnvDhCDZUwqCGjaO1DxZLo0NFaVcVtsaToBZL9RxlOPWWxWkrJ5AsOgdyM7AL74\nn16EWo+Cy+VlJznncHR60fOLt0GNyrj2G88gfGlBC35VhtCFOVz95AmokQ1uEtyEEErRdfet6Hvo\nHoguZ1nb1/V2gla7ed2iIqy00VpRbAlLr+cFgJiakU8rFdCalS1UEgiXW5ZhdA5PHRJCD4AAWt+1\nF20/uacsT3sqUNQ/0I3FZ0bTjwXPzcLZb+wVXzhknv6ub6/GFYQvzsLZ64fU4DSV3VFjMqb+9RLa\n371PK9egBIQA4QtzmPna+silSU4HJKej4HFvZxuaIjHMX74OQrQSENFpR/fdt8HmdcM/0IOlwdHy\nltc4RzJcaGttsbUghPwIQJvBU5/inH+jyuf6EIAPAUBPV3OJrS3WDFnRSjNE0bikjXNNbjPLLEkP\ngIFUZtdUWEhbKSMiTWeHiUhQf08niEAw8mdncOO3nk+VunFwxapfLYXd50HDzl7MXbhm2BdCBAHg\nHJ72FrQc3rcBI7TIZlVBMCHkXQA+A2AfgDs452eqMaitT9aM5HIWN7rQg9ZSWc/84NasBrjS42Tv\nTgU4Hr0b7jdeQus7dpvaYRoh1tmx6/ceACiBEkrCe7D8i2qh3BnD9L9dxuw3Ms5Ce//kzXD2FDaC\nUUnA0qlJLPxwCL6jbRD9DkSuLSA+Eiz7/NWAqSpiC1rjh7OxPn3337i7H/UD3YgvBUElCXafJ/16\nWw/tRV13B8ZePAO1lNMTIXA2Gtf5WWwdOOdvXuUhJgBkL8t0pR4zOtcXAHwBAI4d3WVFPxtJJJYx\nPTKa14tcQyJXF6BGZVCHmGNWwRIq5p68ieYf21Ewx1K7CP9dnRj/0htQQ0nTkjILY/x93VgenkAy\nEkknMYggwNXcgPqdvbB7PYaJEYv1Z7WZ4JKNGNsWWQaoSTZYSWWB7baMxXIxGAeYqnUAl9IL1hvm\nimF2jDLKC8Q6O7p//taKAmCuMjg6vLnyafmBbercZWWGAUgNuRPIxJffwMAn7srRtFTjChafHYWy\nFAcALJ+eKnvM1SQ4Po3p184jo7dE0H7sMLwdLQAAKopwNRk36XHGwMroTCeUwN9rWSBblOQ0gF2E\nkH5owe97ALx3Y4dkURaqaj6/Z8/dct4NMwdu/t5J7PzMfZosmUDAORC5uoi579xE81uMDTS4zGBr\ncSEWqq573XaAigJ6H7oLyyMTCI5NgQoC6vq74O1otXSBNxmrCoI555eB6jUxbSkSCS3ApTQ3AxtP\nZBQdSnb9poJVAoAU0fzlXFsyi8a02l1HieMylhnXCnB2lSe9xTkHSzJQkeQEzYZuQYRodWfgqWY/\nzWxDMKg3JoRA9OcGwaHXZ3HzD06h8/0H4ejxQQkmMfvN65j7zo0KX111SQTDmHr1jYKShsnT59D/\nyL2w6S6CRfY3lVrLovngXgg2q8FiO0MIeSeAPwfQDOA7hJBznPMfI4R0APgi5/xxzrlCCPlFAD8A\nIAD4Euf84gYO26JcZAVwGTzOuWbygZQ7HAiWTmga7jrxsSAufOh78B1tg9TgQPR6ALGhJUAgpi6U\nRKJIzqyv1fxWggoC6gd6UD9gGRRtZqya4LWCAwhFAJsESJI2USWTuXqTZjGomQFGMWKpejBbGZll\nswCYkFSAXqQkoqL6Y23yde8sb5lejSm4/EtPAgKFEohj/1+8BUKbsR4lTxYGhuHzc7j68RPlj28d\nCAyOGNb0csYRGBxD6+G9RfcXXY6yMvSzb1xCeGoGbUf2lwysLbYmnPOvA/i6weOTAB7P+v27AL67\njkOzqBaRqGackY2igC+MAE4f0LEbRBDR8Ke7IX/9Gmb/z7XMdipH8EzeapjKMfvNG2h5+64c9Qg1\noWDphXGoYSsLnA3nHLGFAKLzAQg2Cb6udiv5UOOUDIKr1YixbZsskrK53aaiAlIFShBG6FlgDs05\nqJQzXanMczKpBe4oFOapNOPPkipEr2RY/mC07eJzY1CCmUlXjZtLmMmBmOlzmwk5YjJOziFHS2dZ\nSLmVmByIzi5g+MRL2PGW+yHYK5NYs7CwqAEUVTPysEnaXK6oWpmEtwFw+UFScluiR0Dbu/aC2gVM\n/+vlooec/vfLAAVa3rZLy39QgsVnRjHxpdp2cKs2TGUYf/EMYoFlcFUFEQTMnr+KrruOwt3atNHD\ns1ghJYPgKjRi6MexmizyiccByZNbo8u5NrGZ2d0a1fMmElrThGDiKlQOuk5xIqm5wzlsWhScqjPm\nIAVav9pu5rW8RCCQ6p0lA2A1LkNeiGP6q7l6/YHnx2Fv90DI865nCXXD6nsrxdlUj+jcYkFJAxEo\nHPV+LFwfQmhsCqAE/r5u1PV0aHbLnGPylXMITc1VdD6uqggMj6Fpz47SG1tYWNQm+YkVb2NaelFH\ncIho+YldmPn6VcOVszQcmP7qZcx87SqkegeU5QRYYgMd8jYpi9cGEVtcSs/lupnR+MtnsevxhyyN\n9hrFEqjbSBgHQmGtiY6xjG99xCRDmG2wwbm2TzSuSecYdQ2Xix54hyMApeB2GwihIJSCCAKIqDVT\nGEFS9c75QR5LKFCWE4YucjmnZhyz37iOKx99qkB7cv77g5DnY2CJTEZYjSlYPj2F6PXAyl7rOlPf\n1w2af0NDtHqx4Ogk5i9dR3wpiPjiMmZev4yxk6+Cc46loTGEp+e1/3EFcMYQm69t+1ELC4sKKLb6\nxzlsTUaFxAabygzJ2agVAJuwNDRm2p8Rnq4sWWGxeVitRJphI0ZVRrZdYFwLZPOJxAB3nuC2omoB\nMqWZemK9/nO1zYl64O0yCaZTjWrUIKjVM5ec8cy4BAo1KpecgLmsYvZbNwwleFhcwdWPn0Dj8T7U\n39sFFlcw/8NhLJ0cr/TVbRjJSBSi2wlVltP1Je7WZjgb6rBwdTCnXpinZNQiM/MIDI5WZJuchhDY\nvOVd9CwsLGofHpoCPMYrP0SgUJYS6zyirQkzm485L0vBx2Jzslp1CMNGDIsqoChAMKQ11WXXftkk\nLeur3/2v1GAj+/l41iRpYqdJKEFiOgx7u8e0vCFbg5KIBPZ2D9SEUlDOkD61ypCYi6L1nbsx/+QQ\n5PnC+lkWVzD3rRuY+9bGqjyshOhCAGMvnM5tjKMUktOByOyCccOcqiI4MQ2mrMzSmVBidSNbWGwT\neHgKPJkAHx4COntz5lqWULD08mTF7pwWxrhbGhGamCl8ggOuZmOJS4vNj1UOsZnh0Gq/EkktAHbY\nAacjU/tbTgCc/V3/Wf9SVS0LnW3EIMuGSz6cc01svQLVCC4zsKRqbm9MCZxdPrS8bRf2/dlxePZv\nreaC2TeuFAa6jGF5xNCbIE14ahZ2X3kydDpUFEAlEZ13HLHUISwstgF6AKycOovznzqP0GszYEkV\nSiQJllQRPDuD0b86u9HD3DI0H9it1f1mizYJAur6umDLV+ywqBmsSu5agZDK6351fWJFzZRQ6EG1\nQWDKw1NaBrqpB9ydm/ElhED02sAU45II4/MDgROjaHpsAMRE7xdAWkO491dux8UPf69QlqJGiS+Z\nuNIRUvQ1sqSMyPRs2eex+bxoO7Ifzoa6guYYCwuLrYty6mzKHplh6I9ehtTggL3Ng8R0BPJibSjo\n1Ao2jxt9j9yDhauDiM4uQLBJqN/ZB193e3qb6EIAC1cHkQxF4PD70LhnAA5/ZQkNi/XFCoJrBYGW\nLm0wghBNaWI5VHQzHp4C5CTkk68hPPYG/L/2UwVqEFQStNrfMiTPAK0ebeHZUTQ9auxIlI/gEuHo\n9iE+ur6WxtUitriM4PgUwBi8nW0gAgU3qBXTan+r09hHBAEtB3fD1WRZJltYbHfkxTjkRYMeE4uq\nYHO70H7rQcPnlscmMf3ahfTqnxyJIjw9i667b4O7pXE9h2lRAVbaqFYoZZec/X0lpALgpafnsCj3\ngCeK1KSaOAxllz2ocQWz37iG+PAylk5P5Sg8FKVGzQdn3riM0edfRuDGMAKDoxg7eQa0yllZIlAI\nNglEEEBFEUSgaD6wC562baS7bWFhkU5aWGwOOGOYOXepoPyNqwzTZy+YlwRabDhWJrhWUJmx3bFe\n7gCuSaUZ7lte5+rS03OYWBqAOLxkWL4AAInJMIKvTqPpsQHQ/G0YR3w6BDmQwOy3bqTdiUb//AzY\nB29Bw4M94IyDStpryG6kAwAWV2syCxxdCGBpaDxP6YFBza8HpsT0BqIsCEHbrQchuZxgigKH32dp\nU1pYbDOyV+2Wns61R7bYGBLBsGmgq8QSUBNJiA77Oo/Kohy21BVUiSRw6c9OYOhfzoDJKrrffhgH\nP3YcjiZj692aIxLTXOE4MhlTRdEeB7TGuey6Yf1DGatMIkdZSmDhxAga3tSTY6XJEgomvnIe4cvz\n8N3aCqnRCcEpgSkMUBnG/vYcFk+MFhyPywxjf3MW43//BkSvDYLXht2/+wAgUAh2AUxWwVWO4T89\nXZP1wMHRidJyZoTA1dSI+GJgxXI6hBC4W5tBBWsBx8JiO5IfAE8slVdqZrG2UFEwXYnlnINYc/am\nZcsEwUxR8aPH/xLLV2fSS+83//4ljH/7Ah4/+Wuw1TlLHKEGYAwIhrUaX0q1DHC2kkM8of1utwGE\nahngeFzLIheBh6cK7mLH//YckjMRtPzELoheG+LjIUz+wwUEz2oSMVd+9Wn47+mE95YWLWh+ehiJ\niXDx8yRVyAsxyAsxXPqlJ9H0lgG4dtUjPhbE/PcHkZwtbSO8GWFqGZE755DDkRVVrBBRACEE3fce\nswJgC4ttjhUAbz5sHjcklxPJUKTgOVdTPYSUnbXF5mPLBMET372I0M25nNpTJqtILkZw4+9PYf+v\nPLyBo6syigrAJJuYlAstNYuQLbOTM6lyYPYb1zH7jevG+ykMgefGEHhurIKBZ1CWEpj+t+Ke9rWC\nr6sNoYnpktlgzhjaju7H9NmLhhrBBVAKX2crPO2t8LQ3gwomVtoWFhbbCisAri5MUbA8OonI7DxE\nhwP+/m446iorM+m88yhGn3sZTGXgqgoiChAkCe23HV6jUVtUg5oJguNzIVz7wguYfvYGXB112PO/\n7kfzXf3p56eeugolUtgooMYVTHz/0tYKgqsED4yAc475zz5pTaqrwN3aBFdTPaLzAfNAmBB4OlpR\n19MJm8eNxevDiAWWocTixstohMDV6Ef7bYcs2TMLCwuLNUJNJDF84hSURCKdnFgeGUfrLfvh7+sq\n+zh2nwc7Hn0QoYlpJCNR2H0eeDtarfl7k1MTQXB4ZAE/eOjPoESTYAkFCwSYfPIybvmtt2LPh+8D\nANgaXCASBZcLM2z2Rss8wAwrAF49hBB03X0rguPTWB4ZRyIUgZqlxUwoAZUkNO3dAaaqYLICf38X\n2m47hIUrN7B4fbgwECaAr7fTmkAtLCwAZFbtLKrL3KXrkPOSEVzV1B68Ha0QbOWXMlBRQF1v51oM\n02KNqIkg+Oynvw15OQaud9ZzQI3JeP0z30b/e26Drc6Jgf92B6799QtQ84JgwWXD7p+7dwNGbbGd\nIJTC190Ob0cLQCkiU7MI3ByFKstwtzWjYWcfonMLmHr1QkpjWXsvtx45YHxAxjF/4RrqujvK0mS2\nsLDYumz3VTs1KSMwOIrw1CwESYR/oBee9uaqzI3B8Wnj1ThKEJmZg6+7Y9XnsNi81EQQPPXDK5kA\nOAsiCZh59jq6f+IwfDubcevn3oFXP/51LXvGOTjj2PuR+9H24O4NGPXmhoenNnoIW4rlsSnMXbgK\nJZ4AoQR1fV3oympkSwRDmHr1PLjKcgQwpl+7AEKpYRmFEk/gxveegb+vG417+q2aYAuLbYjeuLxd\nA2AlkcTw0yehJpLgqUbw6MIS/H2daL1l/5qdlysqps9eRDISQ+OeASsZsUWpiSC4mLxItlbtzvff\nia7HDmDiexfBkio6ju+Fu6dhPYZYU2TL7FisntDEDKZfO5+uJ+Mqx/LQOJRoHF1334pkOIKJV143\nbIbjnOcqfOShxhNYvDaI6PwCeu6/w5qILSy2IQWNy9uIhauDUBKJHI11rqpYGhqHf6AHdu/KJFA5\n5wDn8Ha2YnlkwjAbzBQVC1dvIhmKoON2q8FtK1ITQXDPO2/B8FdfBZNzs2VcYZh58SZmTw2h+ycO\nofG2HjiaPNjx/jsx9dRVPP8zX0Hw+hxcHXU48GuPoO/dt237IMLSmaw+sxevFjoFMYbIzDwCg2OY\nPX/ZXA2Cc1BJBFMUU41kzhjigSCi84twN1v2mxYWFtuH0OS0ockQBxCZnq84CFYSScycu4TQ5AzA\nOew+DwSbBKYoxokKlSE0MQ35wC5Iri0gtWqRQ0103Rz5zFvh6q6H6LYBAKhNABEpuMpw9a+ex5X/\n9xk8/fa/xiu//G/gnGP0m2/g+fd/GYHXJ6BGkwjdmMPpj34Nl/6fpzf4lWwCUgHwxf/0WgHwKmGK\nCjWRhBw20TcmBDPnisuhEUFA/UAPJJdL0342gasqYvOB1Q7ZwsKihrDskYH/v707j5PrLA98/3vP\nObX3Wr2qd6m1y5K1WZJ3G2MbGxICEwMZhgwkucxlhs8kc5ObgZAPuZN7Z0JCkg8EmMkQAmSAJDND\n4rAFvGBsY1uSZVmytUutVu/73rXXOee9f5zuUpe6qrt6rW71+/3H7lpOvafU/dZT73ne5xEi87wo\nYMEbh6Vt0/7SiVQADE63NytpUtrcmH0MmiA2uv66mSrzWxcrwZ5ggCdf+x06v/c2/a9eR9qS9u++\niR27WRPYiiTpePot6t6zlzc//T2saHqtXCuS5MKfPc/2f3MfroKN3b5QtdpcGiueoPO108RGx+d8\n3Lxd5ACkZPjaDWdZQ0qn21+Gy3JC19Dd7kWOWFGU9Ua1R3aUNNUxdKkllQ88U2FN5YKOFeobxIrF\nZ8+xUmLG4uhuF1aGOvu2aWGZ5qzblfVvXawEA+geg6YPHOToF59CMzTseIaNROEELV9/jfhQ5s5l\nmqEzfqlvpYeq3MaklFx/7ufzBsA5H8+2nUt905NytpZyEgrrqpflNZXbkxDiKSHEBSGELYQ4PMfj\n2oQQ54QQZ4UQb6zmGJXczAyAN/pVu9KtjXhLixDTG4OFQGgaVXfuwvB5F3Ss2Oh45rb1UhIdGaOk\nudFZiMhg4PyVWZ1VlfVvXawE38qKJrIGC3bSzvpLbJsWnvKNWzN4usyOsniTPQPYc3Tk0wwD27Ky\nB7OL5CsvxfColWBlTueB9wP/PYfHPiylHFrh8ShLMB0Ab2S2aRLqG6SooZbiBoiPT6K5XRQ31OAu\nWPhnucvvQ+haxhQ1t99H2fbNDF9qyfxkyyI6PIa/vHTBr6usXesyCK7/xX10/fD8rA5xut9N41MH\n8FQU0PlPb2Enbn7jE7pG8c4qCjeXr/Zw14SNXmdyuYT7Bua8316hS2ZmNLYix1VuH1LKS8CG3/yr\n3B7C/UN0nTgzlSEmQUJxYy3lu7Yu+ne8sG4TA+euIEkPgoWuE9y+ZZ4cY7Fi87uSP+smHWKmmsd3\nU3aoAd1/c2VM97ko3llF4/sPcNefvp/ggXp0vxvD78Yo8BBoCnL/tz6atzHnkxxtJ/namyoAXgb5\n2h3sWuBlP0WZgwSeFUKcFkJ8PN+DUTLY4JvhrESSrhNnkJaFbVpOfXXbZryjh4nOxde4110G9fff\nheH1oBk6mmEgdI3KvTsIVJYhhMBbWpzxudK28QUz36esX+tyJVjTNR767v/Bjf95mtZvv460bZo+\neIjmDx9B9xjoHoNHf/JJRs52MXaxl4LGIBX3bOxi16oc2vIIbmtiKNvlsgVwFwTwlZcy3tY172OF\nrhHcrv7tFBBCPA9kSg7/jJTyezke5j4pZbcQohJ4TghxWUr5cobX+jjwcYCGuopFj1lZmOmrdht5\nM9xkd+a9O9Ky6D19jsnuPsp3b8NbvPD3x1daTPMTD6Xyg33BYjTjZihUdecuOn5+Km1js9B1yrZv\nVpuTb0PrMggGp0lG8786QvO/OpJ2u5QSM5zA8LsI7q8juL8uTyNUbkdWIklgUyXh3rnTIrIp2VJP\n+a5tGB43UkqsRJJQT/+sxwldQwiBlJKKPdsJVKr6wApIKd+5DMfonvrvgBDiaeAIMCsIllJ+Ffgq\nwOED29bNZgJp24xd7AMJJXuqF1xGK19UDfebrEQSaWepriMlod4BwgPDNDxwBF+Wldu5CCHwBUsy\n3ucLltD40DGGLrUQGxnD8Hkp276ZwtpqpG0z2TtAdGgE3euluKFGXaVb59ZtEHwrKSUt3zjOuT96\nhsRYFMPnYscnHmDP7z6aal27Ean2yMsnMjRC56unkTJ73d+5+MpKqd6/J/WzEIK6YwcID40wfKUV\nKxbHV1ZC6dYmrFgCaVl4gyXortvmz1TJMyFEANCklJNT//8Y8Id5HtayGXitldd+49skJ5wceiPg\n5thf/ks2Pbw9zyPLgQqAU3zlpQhNn7PMpLQsBt6+TOODR5f99b3FhdQdO5B2m5VI0v7SCZKRGNKy\nEJpg+HILtUcPUFCtrpSsV7fNp+u1v3qVs//pR1gRZ+d+cjLOpS+9SGI8yqHP/VKeR5cfM1cWNvqk\nuljjnT2MXm8nGY1jz+hdvxj+LN3eAuVBAuW3tPdexM5nZWMTQrwP+BJQAfxICHFWSvm4EKIG+JqU\n8kmgCnh6KjXMAP5WSvmTvA16iayEiebSEUIQ7hrjpae+hhm5mU9rhhP8/MPf4IlXfpvCLWt/U7QK\ngB2+YAm+YAnR4dE559zoyNiqjWnwwlUSoUiq8o+0JSDpfv0s2979DrTpEm7KunJbBMG2ZXPuc8+m\nAuBpVjRJy9+cYO+nHsNd4s/T6PJDhnqRiTjm8TMbvszOYiSjMdpfOokZiS7bMcfbu4iNjmHG4niK\nCghu24y3pGjZjq9sbFLKp4GnM9zeAzw59f+twJ2rPLRld+Pv3+Ct//fHRHsncBV52flvH8CKJzPW\ngLVNi6tffWXDLoasR0II6u89xPDVG4y0tGUtS7magedEV2/mRkZAuH94wY07lLXhtgiCE6ORtG//\nM+lug4mWQcoPp7dE7H+lhctffolwxygVxzaz6zcfoqBxcXmXYxd7af3OKeIjYWoe3UX9L+xFc+Xv\nW+H0CrCqBrE4Uko6Xl7eABicMmfTpc7i45NMdPWhGTp20sTweSnfvZWSRpXDrihzuf63pzj9O/+Y\n6gqaHI9y8Qsv4K0sTCuLOU0mbSauLi6Hf7WotLXZhKZRvrOZsh1baPnRz7ASiVn3FzfVrtp45lqR\nXsoVQiW/bosg2FXkRWiZKz9YCRN/TQnRvglCbcMUbCmn8/tvc/azP0xNohPXBmj732/y6E8+Scme\nTQt67at/9SpnP/tD7KSJtCRdPzjHxS+8wKM//ncYgfy1Z7ZOn8/ba6934x09JMPLGwBnJCV20qk7\naUZj9J+9iG1aBOfoYa8oG5mUkrf/0z+n5u5pVjRJpHsMzWtgx9JruWoeg7JD9as5zAWZedVOLVrM\nNr0q3PHKKaSUSNtGCA1vSREVe1Yv1ztQVZFxE7O0JYGKYIZnKOvBbREE626D5l89yvX/cTJtctTc\nOpV3b+H0p/6JnmcvoXsMrLjpXDKzbl7WkKaNGYrzzDu+yN5PPcbOTz6Y00pupHecs5/9AdaMSdcM\nJ5i4NsClL73I3k89vrwnqqw42zTpP3shL68tLZuhi9co3Vy/bna0K8pqSk7EiI+EM96n+1xOUwWB\nUwl5+na3zrZfv3d1BrhA0wGwumo3N29pMVuffJhQzwBmPI63tBhfsGTRZU+lbTPS0s5YWyfSsimo\nqaJ8xxYMb/aFq8q9O4gMDjsdQW3nF0zoOuW7tqKrbp7r1m0RBAPs/8P3EB8J0/n9c+geAzthUn50\nM65CLz3PXsSOm9jxubu92AmT859/jsHX23jw735t3tfs/vGFjC2a7ZhJ69+9QXB/PX0vXcNTHmDz\nUwcJNKz8t8XpVAg7vrGLrS/WeEdPxpaamRh+Lw33H0Fzueh46SSJydCSX1/akmQ0hjuwsXLYFSUX\nRsCN5taxzNl/ozJpc//ffpRzf/QsI2c7EQiK92zi6Jc+gK967eXeqy6eC6PpOkX1C7tSm4mUks5X\n3iA6Opaa68daO5js6mXzO+/L2p7eHfCz5Z33MdLSTnhgCMPnJbi1SZWvXOdumyBYdxvc89UPE/3D\nCSZaBgg0BHEXeXl65x/OG/zOZEWT9L98jc4fnmP0rW6iA5NEe8cZOtWO0AQN79vPvlvKPlYAACAA\nSURBVN97HE8wgDRtZ+Uhg1jvBK/9xrcxwwk0t87FP/0pR7/8FI3/4uBynfIsqs7k0sXHJnJ+rBVz\nvmgYbheNDx6h++RZIoMjSxuAlKogu6JkoRk6Wz96Ny1fP44Vu3nVT7h0ggfq2fTwDjY9vIPEuJPO\n5C7OT4fH+Ux38VTz9OqLDA4THR1PX+yQEiuZZKSljco5UiwMn5fKvTuAHSs/UGVV3DZB8DRfdVHq\nW//4pT40l76gIBjATli8+rFvAU6qxEyt/+Mkvc9f5snXfoeax3dx5g9+OPsAmkDaNmbYSh0PLE5+\n8n+x6ZGdK1KpYqUDYNs0Gb3eznhnLwIoaqyldEvDqpeFSUaijLS0ER0Zx10QILitaVFdg6bZlsVE\nVy/h3kF0jxthGAhNTJW/mZvQNZLhKO6AH93tpuH+IwxeuMrwldZFjUVoGgU1laousKLM4c4/eJJY\n/ySdP5y66pe0KN1by/3f/tepx6zV4BdUAJyJGY1h2zYuvy9rioMZi2PGE7gDvrQObwsVHhjOXH/Y\ndppwzBUEK7ef2/rTNtBQOiuIzcVcz7GTFrHBEO3fPUPzrx5l1ycf5PJ/fTlVnk33ubATzia5Wwld\no/snF2n64CHGL/YRHw0T3FeHq2h5Os5Yp8+vTABsWbS/eJJEKJzaBTt04RqTXb00Pnhs3vxVMxZn\n4PxVQj19SAmFNZVU3LFjwZ12YmMTdLx8EtuyQUpio2NMdvey6fA+imozdZKd+5xsy6LjxZMko07x\ncwQgBOLWpMJsxzAt3IXp9Xwn5+skJwTugJ+Cuioi/UPEJ0IIoSGljS9YyqaDdyzoPBRlo9HdBvd8\n7cNEuscYv9JPoK6Uou3rqzzVWgiAp69iLjavdjkkQmF6Xn+L+EQIBOguF9UH70hrPmElk/Seepvw\nwLCzQCElwa1NlO/etqixay4DNA0yVHTQ3a4lnY+y/tzWQbAR8LD5w4dp+evjy3pcK5Kg5/nLNP/q\nUfZ95gmqHtjGtb9+jfhwmLon93Dhz35KfHj25g1pS8Kdo/zo6J8Q6R5HMzTshMnu//AIe/7vd+Z1\nMprLRGcviXAkrQyMtG3iE2Eme/opqsuep2WbJm0/O44Zi6dqLE509RIeHGHLo/ehu3KfdPrePJ9e\nB1Q6m8l6T5/DUxjAUzT/inC4f4j+ty6RCIWdfO6Z6SwSkBIpnFXZ+cveSMxoLBXMx8cnSYYjWR9d\nsXcHZds237xh93biE5MkQhHchQE8hQXzjl9RFIe/tgR/bebWt9OshMnlL71EyzeOY0YSVD+0jX2/\n/8S6aJyxUqxEgv63LjPZ3Ye0bbzBEqrv3IV3Ee2Hl8I2ncWVmaXPTCtO98kzND54LFVDvfvEGSLD\no2BLppt1jrS0o7tdBGfOpzkqrq9h+NL1WcscQtcp3aIq82w0t/0W9EOfey+6f5m/3ekCX9XNjRZV\n92/lvm/+Ko/84BPs+MQD1D65B2HMfmulLbn6V68y2TKEFUmQnIhhxUwufvFntP/D2SUPa6U2w032\n9GW8fCQti8nu2SVjZhrv6MFKJGcFm3YyyVhbV85jsE2T2PhkxvukaXHjhdecYHuqDm8mkaFRuk68\n6QTAkLHwOeCsNuTyhURC/9uXUz/GxiZwlpMzi49Pzsoh9xQVUlhTpQJgRVlmUkpe/tDXufBnzxPp\nHiMxGqHze2/zzMNfINQ+nJ8x5bkesJSS9pdOMtHdm/qSHxsZo/3l14kvw8behZjs7nMqLdw6RstO\npZQlQmGiw2Opagw3H2MxfKU1656cubj8PqoP7kFoGkLXQNOcmsMNNRTWVi3uZJR167YPgjXD4K4/\n/xdo3uVb9NbdBls/diz1sxlNMvR6G2MXe5FSsvfTj+Mu9aN5bubL6n43te/a7ZRwu+UP14okuPjn\nP130eKbL7Cz3JTYpJZGhUax45m49AGY8QXhwOOtkFB4YyhJA20QGFvJBNE9QaktiY+N0vPpG1rEM\nXryae+UHT25fnGKj4zef4/POOczJ7n7C/UM5HVdRlKUZPtXO4Mm2tLKZ0paY4TgX/nTx8+1irYU2\n9qG+QZLR2Oyg0rYXvZdhseKTocy5uUB8wlnwSIQjWdPtZi2uLEBxQy3NTzxE5d6dVN6xnaZH7qH6\nwJ41ezVWWTm3dTrEtM0fPIy7yMfZ/+dHi+8cJJxAFsvm4B+9l5LdTgrAtW8c58xnvo+dtJCmjdA1\ntv763bzrxd+i5RvH6Xn2Mp7yANs/fh+R7jF6nr2U8fDR3vGMt89npcrsJCMxOl55HTMWn3OTWGx0\njO7jb6IZBvX33zVrRdPw+WbV7QRATAWNOdIMHX956dzVFyQkw1FioxP4grMv7cWzrCTPOoyUuHxe\nzEj2VeWZ45rmrwiiu1yYGVq3grN6MdbWlZbvpijKyhg4fgM7MXtTtLQkfS9eXdWxrJU29rHRcWSm\n+UlKZ8V1FbkLAwhdzxgIT6e2uf1+bDPzxnbd415SPXXD46Z0S8Oin6/cHjZEEAxQ+8Qeap/Yw4sf\n/Br9L7WkVYwQXh0ZtzLvhdIETR88RPmRRlx+D5se3Ymn1Knu0PfiVc783vfTSvVIy+baV19l4nI/\nD//Tv2HfZ55I3Td0qj3rN83i3QuvfyhDvVinzzPy4+5lX1noOv6m0zVtnm/a0rKROPldnT8/RfMT\nD6WdY+nmesanCpLPJDRtwRNQ9cE7aH/xBLZpZV1BEALioRDjHd1MdHRjWza+YAlV+3ZieD0kknNX\nChGahpSS6Mj8HwhC0yjZfLMTlRCChvuPcOOFVzN/0ADSzny7oijLy1PqR3MbWObsNDFPcJXrcK+R\nNvYunzdr4OnyL88G7VwV1W5i8NxVrFvGInSNsh3O+xQayH7lLLi1aSWHp2wQt306xK3u/euPUPPo\nTjSPgVHoQfca7PiN+6i8rzlj4wtXoZejX3yKbR+9m6YPHEwFwAAX/vyFtAB4pv6ftzB0si3ttrLD\nDRTvrkbzpH/30H0u7vz9J1gJ4cFhuo6fpu2F1xg4f8XZoDaP+GSIRCiUOQAWApGlLJptmkSG0ldq\nPUUFzmUmXUMYOpqhIzSNqjt3pzY+5Mod8LPlsQeo3Lsja4kc27IZvdrGeFuXs4lOSqLDo7S//DrF\nDbVODtgtNJeBv7KMooZap/OPlLO+EAlDR3e7nPHrTi6Zr7yU8l3b0sdY4GfTob3O7uNbCF2nqK5m\nQeesKMri1L93X8bbdb+bHZ94YJVHszYU1m3K9DHnBJ7bVzdA1wydxoeO4i0pSuXnGl4PtUcPpD4b\nxm50Zn1+prlcURZqw6wET3MVeLj/Wx8lNhQi2jtBQVMQV6GXiZZBnnv0L7BiSayYiTA0NJfOwf/y\ni7R++3WklNQ+vjttN3J4rs0VEq785c+pOHZz96oQgof/8eO8+env0fbdM0jTomBzOYf++JeouHvh\nu1zn03P6HBPt3amfY+OTjLV10fTw3XN2JLPiCadsF1nyZ2Xm2yU3G0ikbpOS4oZaCjZVERkYQgKB\nyrIFVYWYSXcZlG5pwPB66Dn1VtoKs9A0PMUFxCfCsyo7SMsiOjpOcNsWRq62plZ8DZ+H+nsO4S4I\nYMbiXP/JS1nOGervPUwyGseMxfAFS7IG8YU1VfjLSoiOjKdWXITujK2obmGl3BRFWRx3sY/7v/1R\nXvnIN0EIZ66QkqYPHKTpg4fyPby80F0G9ffdRdfxN1Nzk5SSij3bCVStfsUMd0GApnfc49QJtmxc\ngfQ6wdmuqCFEDtV7FGV+Gy4InuYtL8BbfjN/tWhrBe8+9R9p+eYJhk7eoGBzOUbAzan/6x8QmvNH\n+ebvfZ+9n3qM3b/1DgBK99US7hjN+hqj57pn3eYq9HL0yx/kri8+hZ2wMHyLCwbna488dPl6WgDs\nPEliJ5IMnr9C7dEDJEIRzFgMT1FhWn1ET3Fh1gnG8HkwPJ60DWEptsQbLEZKyWhLO8NXW7HiCQyf\nl4o92yhuqF3Uud7KjMWdzj37djF8qQUznkAIQXFjLYbPS2z0WsbnRYdHqTt2gODWRmJjE8THJxjr\n6KH1+VenVn/nSv2QaIZBYc38ZYSEENTfe5jxjh7G27tBSooaaihurFtSDpuiKAuz6eHtvO/KH9D9\nzCWSkzGqHthK4ebVC/ZmboZbK3zBErY++TDRkTGkaeENliy5QY+UklBPP+Md3UjplCErrK3Keb7L\ntj+koKaS0esds+ZmoQkClRu3zJ2yfDZsEJyJt7yAO37nnQCMvN3N8+/68qxuc+f/5Dkq7212utJp\nc+8kdc3RtUjTNTTf4gKi+brD2abJ8OWWrM+f7B2k7cUTxMcmUvVwS7bUU9xYR3RoBM0wKNnSwNiN\nzrTcMaFrVO7dieHx0PnqqfRVWF2jsKYKd8DP4IWrjLS0p55rRmP0nbmAbVpL2ohgmxa9p98m1DuY\nGndRQw0Ve7ahu1wITZvz8tl0YK+7XcQnJhm8eC33ahFeL66C3PMIhaZR0lRHSVNdzs9RFGX5GQEP\nje/fv+qvm6829lJKRq+3M3L1htNhrcBPxZ4dFNbcbCgihMBfVrpsr9d98gzh/pud2CKDI4y1dVF/\n76ElffEv276Fic5erGQyVdFC6DqFNVULTqdTlExUEJzF9W+eyNhu2YqZXPyLnzHwynXMUPb8WuHS\nKD/cyNAb7ZQdrJ93IrBNi+4fX6T/5Ra8lQVs/tBhAvWzJ6mZE6uzy3j2TuPY2MRUfnOWlU3bdlZy\npUwFhqPXOxi93o4QWuq5xY11hPsGMWNx3IUBKvZsT1U2qL/3MAPnrhAfn0BzuSjd2kjZ9i1YSZOR\na20Z0hFshi5ew1dWSjIUwV3gxzPV7tg2LUZvdBDqHcDweiltbsg4QfeePkeodxBp26njT3T2oBk6\nVft2AaDN0fHHntoUZ1sWgxdyC4CFriGERu3R/ap8jqIouVulAFjattNMQkp8ZaUMXrzGWGtHan5L\nTIbpOXWWTYf2ztnYaLHC/UNpATBMpZ+NjDHZ3U9R/eJf0/B62PzIvYxcayPUO4DmMihtbqCoXu2t\nUJaHCoKziA+HMpcGk5KhU+0kJ2bXWkx7mCm58fdvcOPv3nDykP/2Y5QdqM/42ORkjOef+AqhtmHM\ncALNrXPhz37Krv/wDiKdoyTHowQP1NP0gYP4S262R84UAAO59VW/9dL/1M9yRr7veHsXW9/1kLNZ\n7Bb+8iBND9896/ZEKJy125qVSNL2s9fQNB0pbbwlRVTduYf2l46nBaST3b2U7WgmuLUJzWUghMCM\nJwj1DmQMrsdudFKxZzuaPrXxLsvu5+kdIfHxSYQQOTRGhuD2zQSbm1Q7TUVRFmylA+BQ/yA9r7+V\nWu+QUmatyz5w7gqFtdXL/mV+oit7M6Xxzu4lBcHgBMKVe3dQuXfHko6jKJmoIDiLmsd30/v8FczI\n7JzbeH8O9WalxJx0VorNUJyf/dJ/573nfx9XoRczmqT/JWclsvK+Zi58/jkmWgaw485EYiec/174\n3LOpw3X98Dxv/38/pvaxZo58ZO7cWk9xIYbHQzISnX2nJpwAMMc0gInuvgWlMBhez9wbFmyJbTsr\nstHRcdpffG32lw0Jw5evM3yl1ZkA9+3CcBsZq3dMsxJJNJ+OvzzodEPO8Jjpy4G625UW7GclwOX1\nqgBYUZQ1JxmJ0n3iTM5zuRmLY5vWkvN/bzVXTK2unilrnQqCs2h8/34u/cXPCLUNp4LTpbBNm46n\n38JTXsDxj38ntdnOTlqgabm9hoSen7byxsQ423cFsz5MCEHdPQfpePl1bMtO/5ZuS2ROa6DO6oHT\nLMNOpXNYSZOhS9eY6OjBtm0CVeVU3rEjVW3C5fPiDZYQHZqjqUXaWOYagMSMxug5eSZzw40Z52tM\nrVZruk71ob30vvF2+oeDAN9UioWVSM7ZACT1FE1XG9kURVkwGeqds6WvbVokI1EMnydrpRzbsoiP\nT6K5jIxt1cfauhbUNlhoAm0FyooV1ddkXA0Wuk5xg9oToaxtKgjOQve6eOzZf8+5P3mOK1/JUjZr\nAaxIgrELPVz/1utpbTwXSpqS7teH8ZbdiT47SyHFU1RI8xMPMd7eTf/bl+ZM3ZjL8JVWRq7doLix\njoo92+l4+SSJyZslyELd/UQGhtn8zvtwTe3w1d0r8GuVLQDWdYLbN6cFq0W11UQGhtM3yUkYvHAV\naUtn02AuHx5SUrBJdXdTFCV3093hMjXHkFIyeP4qo63tzhKqLSmsrab64B60GfXXR1s7GDh3xblq\nJ21cfj91dx/AXRBIPSYZjuY8rwtNc+qkr8CXen9FkMLaaia7+2aUhNQJVJZRMGMznqKsRWqZaw6u\nIi9bfuUwRqFnyccyCjxEBydzvnQ1J6Hl1PRC03UmOnsXHQADzuY5y2a8rYvOV06RCEVmpTvYlsXI\ntTbn/02TcF/2Lj/LSTN0ynZsoWxHc9rtVtJkvGN2eTpp2QxdupbT6onQNaoO7EF3z/FNQ1EUZQY5\n2p41AAYYunSd0dZ2p9OmaSFtm8nuPnrfOJd6TKhvkIFzl5GWhW2aSMsmMRmi/aWTaXOvr6wkc+Oi\nDCkI7sIAlft2Ls9Jzno5waZDd1B390GKG2opaqih9uh+ao8dUOkQypqnVoLn4asuWnI6hHBp+KqL\n0D2uVL7vUkgpc2pxaSWSREey1zFGgDdYQiyHnvHStp2qE5kCSFsSmWpvaSVMJ3UhF5qG7jKwstQ6\nnk/dPYcYudZG6zMv4y7wU7azGX950Nmcl6XZx1xfQnSvh4LqCgyPm+LGOtwLKImmKPkmhPg88AtA\nArgOfExKOeuPWwjxLuCLgA58TUr5uVUd6G1srjb20rYZbbkxaw6Stk2od8Cpf+71MHT5esZ5yrYs\nQr0DFNY6DXeKG2oYvnwd89ZNaRnm6EQoTHwihK90/jrniyGEIFBZRqCybEWOrygrRa0Ez8NTFqDm\nsZ2zWh1rXgPNa2QM+DSvQdH2StAEmkun/hf28egzn6Tqga0YgQwri5rI/V9C0yhtbsypAoRtmswd\nkYoFdW4TQmTdBTF9mc3wuhFa5rbKAGIqVcLweanev5uybYvrlKe5DDpffYNQ7wDJSJTwwDCdr77B\neGfP3JvzhMjY0higoLKcTQfvoGLPdhUAK+vRc8AdUsp9wFXg07c+QAihA18BngB2A78ihNi9qqO8\nTclQ75z3W0kz614EoWmpjcwZNzTjfIFPhKNER8YI9Q8ibUnjw3dTUFPlzGvCWfHNVL9eWjYjV1sX\neEYZziGRdGr2KsptQq0E5+DYf/sVXv3Ytxh4pQXNbWDFTWoe3cWe336En3/kb0iMREADO2ZS/959\nHP1vH0I3dOykhdBFKkBsfN9+zv/xc1iJMWRyKkjTRM7pCpohCG7bQtnO5vkfjBNo6h43VrbUCSkJ\n9y8gdUGQNZc2NjZBbHwSb3EhFXu2MfD25cyBqCXZ9p53pNIMxm50InRtYWkiYqrB260rKpZN/9lL\nbHv3w/jKSokMjaSNV2gantKizCvfmkbZztUpZq8oK0FK+eyMH08Av5zhYUeAFillK4AQ4u+B9wIX\nV36EG5vuMrKWj5S2jcvvNFfyFhUQzjBnC00wfLWVocs2AqdtcNnOZuqOHUileA2cu0KipS3j68cn\nw4see2xsgt7T54hPhJwxlhSx6fDejBv2FGU9WVIQnOvlt/XOVeDhof/9G4Q6Rgi1DVPUXIG/tgSA\nX3zr9xh+o4P4SJiyQw1prZg1V/qKqO518fhPf5Mzn/0Bnf/0NrZlEzxQx9j5HsxQ5pSA6oea2PFr\nd1IU7ePqT0qy5liF+gcZvdaGGYvjKw/iLSlCd7uo2LOdvjPnswfaue4uFiLVXjlb4Nz2wmvs+KXH\nKN3SwMi1G87GjdkvyETXzbJrgaoKkJdyG8MUX7CE6EiGts0A0iY+Gab26J10vnZ6qiaw88HjqwgS\nG8n866m7DFwBtfqr3DZ+DfifGW6vBWa2VewCjmY6gBDi48DHARrq1AbRuczXxh6cL+HBbU0MX72R\n3olT0yisrcLwOntPyndvIzI8ekt1G6es5fRt07P28JVWPIWBVIqEt7gQYehI85YUCSHwliwuFSIZ\njdHx8knsGceMjY7T/uIJmh97IGMdeUVZL5a6Evwc8GkppSmE+GOcy2//cenDWpsKGoIUNKSXJhNC\nUH5XY87H8JQFOPaVD3HsKx8CINQ2zD/f/fmMj3UVe3jga+/GOnGWC8+UZq3HOHS5heErNyfW6W/r\n6BoCKG1uJD61UmsnFnEpSwjcAT+G10M0SxAJgJR0n3qLuiP7yZaGIS07LQfY5fdStnMLQxezt3lO\nG8pUCbQbz7+SsWKEtCWaoaO73TQ9dDfx8UkSkSiewgDSsml78UTG49pJEzMWT1W4UJS1SAjxPFCd\n4a7PSCm/N/WYzwAm8J2lvJaU8qvAVwEOH9i2hN21t7eFtEcu29mMtG1GWtqnniwpqt9E1f6bGSm+\nYAm1xw7Qf/YiyWgMgVPtJzYZgllXvyyGr7SmguDCumoGLlzFsqy0+VFoGmXbF5d6NtragZ1hEUVa\nNmNtXZTtUFfQlPVrSUFwjpfflDkUNJVRfnQzg8db0zfNTaUeHH/vd/A0HcTIUqDCjMUZvtyaJfXA\nRgJjrR003H8UX7CYqz94PtU+OI2m4fL7SIYyXDKTkkQoTCLTfbcIdfVhHUjirwgyHonMClSFruML\nlqTdVr5zK7GRcUJ9g3Me21Xgp/auO/EUBPCXlRIZnF2L2F0YSNUsBqdxyHR75kQ4knXlWyLTShQp\nylokpXznXPcLIT4KvAd4RGYug9INzGxdWTd1m7IIubSxn0kIQcWe7ZTtbMaMxtE97ozNKwqqKgg8\n9gB2IonQdUZvdBA7fzXjMWdWCtJ0naaHjtHzxttEh8cQAlx+H9UH78BTtLjUhdjIGGRJ4YiNTSzq\nmIqyVixnTnC2y29KFmMXemn77psUNpdjRuIMn+0CSzqpCxKSEwm6LiXQW19j8yP3ZuxcFhkcmcor\nzv460rIZvd6OL7iPgk2VTHT2zA5OgfJdzfSePp9xwsuZEMRGxinbsYXJrr6pzXlTd2kanqIC/FM7\niGNjE0QGR9BcBsHtm+cMggNV5dTdcyiVDrLp8D7aXzyBlUwiTQth6Gi6Tu3R/VmP4Q74cRcEiE/c\n0vFPCHzBEtUZTlnXpqo+/C7woJQykuVhp4BtQojNOMHvh4B/uUpDvC3N18Y+E03X5918K4RIpRp4\nS4qcfOIM7Ym9t1R8cPl9ND5wdGojnp1qJLRY7sICIkOjsxcQNM3ZiKco69i8QfByXX5T+WXpzv/J\nc1z8wgvYCWfHsOFzU7qnhokr/WnNNKQNVjzBSMsNfMFShBD4yoOpzj9iKuVhvmuVyaiTn1uxZzvh\n/iHsqQnSOYZO+a6txMcnlxYAM9WVyGXgDvhpfPgYg+evEh4YQtN1ihtrKd+1FYDuk2cI9Q0ipUQI\nDZAYBX7MUObPbl9FMC0f2uXz0vz4A4R6B4hPhHAX+CmoqZp3Nbf26H7aXzqJbVlIy0IzdDSXQc3h\nfUs6b0VZA74MeIDnpv5WTkgp/08hRA1OKbQnp1LXPgk8g1Mi7etSygv5G/LGExubIDI0gu5yUVBT\nlVMbY395EHdhgMTEZFqFCaFrqTn1VkttjyylJDI4gjB0pxX9rQsnQlCyuT7zkxVlnZj3r2QZLr9N\nH0fll00Zu9jLxS+8kBbsmpEEY+d6MlZJkLbN8OVWNENnun9wzV13UrCpEm9pMfY8m9vEVKrDjZ++\nSmIyhOZy4SsPIk0Tw+uheHM9Apjo7puzPXEudLc7tTLhKSyg7u6Dsx4zcr3dCYBTmzysqfcgc2kg\ngPHWTko3N6RN7M6GkmoKa3Mfn7swQPMTDzLZ3U8yHMFdWEBhTaVqj6yse1LKjNGQlLIHeHLGz/8M\n/PNqjeu2Ns9muJmkbdP9+luE+weRcqrk5NmL1B07QKCqfM7nCiFouP8IA29fYqKzF2nbeIqLqNq/\nC29J0XKcSRorkaTj5ddJhKeaIwnng0HoGiDQ3QY1R/arPRTKurfU6hC5XH5TbtH23TPYidl5ufOV\nCZu5O7fr5BmK62uZ6OxxWmvO8TyhCSa7+lIrv1Y8QXR4hOKGWnzlpXSfODPVnlPmHAALXUNzuZxN\nblKCrqFpOnV3H5y3S9DY9Y6M5yqEhn9TGeHegVn3mbE4Y22di64rPJOm6xQ31Cz5OIqibFxytD2n\nzXDTRls7nAD4lgoPXSfOsPXJh+ddudVdBpsO7aX64B0AK9qNrf+ti8RDoZtVhaYWWnSXm7p7D+Ip\nKlTd4JTbwlJzgjNeflvyqG5zdix70fScV2Jt6bQGvmUVWHe7cRcFiI6MI4DApgriY5Mkw+nfUaZ3\n9o63dyNte0GLv0LXKarbRNWB3USHRomNTeDyeXJKRwDS8oTTxiRl1u5x0rYJdfcvSxCsKIqyWAup\nBjHTaJYv/wgI9Q7k/MV8pYPP6VbOmcpqWskkyJUfg6KslqVWh8icjKTMqe7de7j+NycwI+kBn+41\n2HSwlJ6TQ0g5tbo7V45uhjQI2zKp3r8bT1Hh1EMkV55+JvshcskBFkytFDu5acHtTQQqyxfdKjNQ\nWe4E8Le+jAB3gZ/Y6FjGLwLaArrbKYqirJTpzXC5BsCQ/cs/tsReQ13YpJRky2wUQmSuLqQo65Tq\nGJcHFfdsofqRHfT+9ArWVCCs+1wE6ou46xO7GGzo4tplL7ZlEe4fJDIwuxRYNkJoJMPRVBAshEAz\njMwTcK6NMhAEt2+hfGfzsuTOlu/eymRvf9pkKnSNguoKyrZvZrK7b9aKidB1tQlDUZR1K1BZxkRn\nhtbKAvwVTv15adsMX73B2I1ObNPEXx6k4o7tq9qZzalcUUBiMjTrPiltvKXLn4OsKPmidgPlgRCC\n+775EY588Skq72um7FADd372SR77wYfRhIXHb1CyuZ7YyBiRoQzNKQTZelEgW7ySwgAABvlJREFU\nbRv3LfUgS7bUZwxetak2nvOSksjQyLJtHnP5fWx+5F6KG2sxvB7chQEq7thBzZH9eIoKqdi93Xkt\nTQMhELpGcUMNBZtUVRFFUdan8t3b0Iz0dSeh6xRsqkotWnQdf5PhK9cxozHspEmod4D2nx3PqUb7\ncqrav2tqE1z6WMt3zT4HRVnP1G9znghNo+mXD9D0ywcAJ89MJuIMTV1iiwwNE+obypgO4Skuorix\nlsHzV9JWTIWmEagqT2sWAVCxexuJUIRw36BTUxgwPG5q7rqTzldP55QSYUZjSzndWVx+H5sO7c14\nX3BbE4W1Vc6KsC0pqK5INbxQFEXJl5ntkReSCgFOnfKmR+5h6FIL4YFhdJdBaXNj6gpXdGScyNDo\nrKtgtmUxdLGFmiN3Ltt5zCdQUUbjA0cZvNRCfGwCw+elbEczhTWVqzYGRVkNKgheA+RoO1JKhv7z\ns6mJNdTdn7EwOoCmaQSbG9EMncHzV7GSSYQQFDfWUbl3x6zHC02j7tgBEqEwsbEJDK8HX5lTc7jp\n4WP0n71EeGBozjH6y4Jz3r/cXH4fQbUJTlGUNWJ6ocI8fmaqO9zCuQP+rDXJo8MjSJlhQUJCZCj3\nlLjl4i0tpv6eQ6v+uoqymlQQnGcy1It1+jwjP+5OX1mYI/Vg+jJVSWMdxQ212EkTzdDnTVdwFwRw\nFwRm3VZ/32Fs22a0tYPBc1dm5QoLQ6dsp+oPryjKxjS9AjxzoWK56W630xXOnr34oaluloqyIlRO\n8BpVXL9pVk4WOHlZxU11N38WAt3tWnK+rqZplG1tYtt7HqGkqQ6h6yAE/vIgjQ8cnRU8K4qibCTW\n6fMrevyCmqqMtwvdufKnKMryUyvBa5S3tJjS5kZGr7encsSErhOoLKOobtOKva7uMqg+eEeqILui\nKIqy8nSXQf3dh+g6ftqpEDnVvKioviZt4UNRlOWjguA8mrnJIpPKO3ZQVFvNeEcP0rYprK3GXxFU\nhcoVRVFWyXzz9HLyVwTZ+u53EOobxE46JdLcBf75n6goyqKoIDhPcu065C0txltavMqjUxRFURbb\nHW4pNF2nqLZ6xV9HURQVBOfFzInV2WWsyn8piqKsJfkIgBVFWV0iW3vEFX1RIQaB9hweWg7MXbvr\n9qbOX53/Rj5/WJvvQaOUckN1bplnzl6L/0b5ot4Lh3ofblLvhSPf70PGeTsvQXCuhBBvSCkP53sc\n+aLOX53/Rj5/UO/BeqD+jW5S74VDvQ83qffCsVbfB1UiTVEURVEURdlwVBCsKIqiKIqibDhrPQj+\nar4HkGfq/De2jX7+oN6D9UD9G92k3guHeh9uUu+FY02+D2s6J1hRFEVRFEVRVsJaXwlWFEVRFEVR\nlGW35oNgIcTnhRCXhRBvCyGeFkKU5HtMq0kI8ZQQ4oIQwhZCrLmdlStFCPEuIcQVIUSLEOJT+R7P\nahJCfF0IMSCEOJ/vseSDEKJeCPEzIcTFqd/938z3mJS5bfR5eqaNOmdP28hz90wbfR6fttbn8zUf\nBAPPAXdIKfcBV4FP53k8q+088H7g5XwPZLUIIXTgK8ATwG7gV4QQu/M7qlX1TeBd+R5EHpnAb0sp\ndwPHgH+3wf7916ONPk/PtOHm7Glq7k7zTTb2PD5tTc/naz4IllI+K6U0p348AdTlczyrTUp5SUp5\nJd/jWGVHgBYpZauUMgH8PfDePI9p1UgpXwZG8j2OfJFS9kop35z6/0ngElCb31Epc9no8/RMG3TO\nnrah5+6ZNvo8Pm2tz+drPgi+xa8BP873IJQVVwt0zvi5izX0R6OsHiFEE3AAOJnfkSgLoObpjUvN\n3UpWa3E+N/I9AAAhxPNAdYa7PiOl/N7UYz6Ds6z+ndUc22rI5fwVZaMRQhQA/wD8lpRyIt/j2eg2\n+jw9k5qzFWVh1up8viaCYCnlO+e6XwjxUeA9wCPyNqzpNt/5b0DdQP2Mn+umblM2CCGEC2fC/I6U\n8h/zPR5FzdMzqTk7KzV3K7Os5fl8zadDCCHeBfwu8ItSyki+x6OsilPANiHEZiGEG/gQ8P08j0lZ\nJUIIAfw1cElK+ef5Ho8yPzVPK1PU3K2kWevz+ZoPgoEvA4XAc0KIs0KIv8z3gFaTEOJ9Qogu4G7g\nR0KIZ/I9ppU2tcHmk8AzOEn0/0tKeSG/o1o9Qoi/A44DO4QQXUKIX8/3mFbZvcBHgHdM/c2fFUI8\nme9BKXPa0PP0TBtxzp620efumdQ8nrKm53PVMU5RFEVRFEXZcNbDSrCiKIqiKIqiLCsVBCuKoiiK\noigbjgqCFUVRFEVRlA1HBcGKoiiKoijKhqOCYEVRFEVRFGXDUUGwoiiKoiiKsuGoIFhRFEVRFEXZ\ncFQQrCiKoiiKomw4/z8Mq8DJ9XsnywAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"R3OK8p-Ng3BC","colab_type":"text"},"source":["# Activation functions"]},{"cell_type":"markdown","metadata":{"id":"ghf5uLuhg3D0","colab_type":"text"},"source":["Using the generalized linear method (logistic regression) yielded poor results because of the non-linearity present in our data yet our activation functions were linear. We need to use an activation function that can allow our model to learn and map the non-linearity in our data. There are many different options so let's explore a few."]},{"cell_type":"code","metadata":{"id":"ivnfSKEhg3Md","colab_type":"code","outputId":"2068067b-2085-4dff-93ca-d6cb8743de3b","executionInfo":{"status":"ok","timestamp":1583943917186,"user_tz":420,"elapsed":1172,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":227}},"source":["# Fig size\n","plt.figure(figsize=(12,3))\n","\n","# Data\n","x = torch.arange(-5., 5., 0.1)\n","\n","# Sigmoid activation (constrain a value between 0 and 1.)\n","plt.subplot(1, 3, 1)\n","plt.title(\"Sigmoid activation\")\n","y = torch.sigmoid(x)\n","plt.plot(x.numpy(), y.numpy())\n","\n","# Tanh activation (constrain a value between -1 and 1.)\n","plt.subplot(1, 3, 2)\n","y = torch.tanh(x)\n","plt.title(\"Tanh activation\")\n","plt.plot(x.numpy(), y.numpy())\n","\n","# Relu (clip the negative values to 0)\n","plt.subplot(1, 3, 3)\n","y = F.relu(x)\n","plt.title(\"ReLU activation\")\n","plt.plot(x.numpy(), y.numpy())\n","\n","# Show plots\n","plt.show()"],"execution_count":47,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsIAAADSCAYAAABAW6ZrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIglJhbCIiIj8/0bBKBiRAADqlepjG3utPwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 864x216 with 3 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"b_yo9fwwciDY","colab_type":"text"},"source":["The ReLU activation function ($max(0,z)$) is by far the most widely used activation function for neural networks. But as you can see, each activation function has its own constraints so there are circumstances where you'll want to use different ones. For example, if we need to constrain our outputs between 0 and 1, then the sigmoid activation is the best choice."]},{"cell_type":"markdown","metadata":{"id":"u72au7H9oHk7","colab_type":"text"},"source":["**NOTE**: In some cases, using a ReLU activation function may not be sufficient. For instance, when the outputs from our neurons are mostly negative, the activation function will produce zeros. This effectively creates a \"dying ReLU\" and a recovery is unlikely. To mitigate this effect, we could lower the learning rate or use [alternative ReLU activations](https://medium.com/tinymind/a-practical-guide-to-relu-b83ca804f1f7), ex. leaky ReLU or parametric ReLU (PReLU), which have a small slope for negative neuron outputs. "]},{"cell_type":"markdown","metadata":{"id":"xqWpi0X2dWpe","colab_type":"text"},"source":["# NumPy\n","\n","Now let's create our multilayer perceptron (MLP) which is going to be exactly like the logistic regression model but with the activation function to map the non-linearity in our data. \n","\n","**NOTE**: It's normal to find the math and code in this section slightly complex. You can still read each of the steps to build intuition for when we implement this using PyTorch.\n"]},{"cell_type":"markdown","metadata":{"id":"0prEPVOlv1F4","colab_type":"text"},"source":["Our goal is to learn a model  𝑦̂   that models  𝑦  given  𝑋 . You'll notice that neural networks are just extensions of the generalized linear methods we've seen so far but with non-linear activation functions since our data will be highly non-linear.\n","\n","$z_1 = XW_1$\n","\n","$a_1 = f(z_1)$\n","\n","$z_2 = a_1W_2$\n","\n","$\\hat{y} = softmax(z_2)$ # classification\n","\n","* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n","* $W_1$ = 1st layer weights | $\\in \\mathbb{R}^{DXH}$ ($H$ is the number of hidden units in layer 1)\n","* $z_1$ = outputs from first layer  $\\in \\mathbb{R}^{NXH}$\n","* $f$ = non-linear activation function\n","* $a_1$ = activation applied first layer's outputs | $\\in \\mathbb{R}^{NXH}$\n","* $W_2$ = 2nd layer weights | $\\in \\mathbb{R}^{HXC}$ ($C$ is the number of classes)\n","* $z_2$ = outputs from second layer  $\\in \\mathbb{R}^{NXH}$\n","* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NXC}$ ($N$ is the number of samples)"]},{"cell_type":"markdown","metadata":{"id":"pB1N3X34EHLS","colab_type":"text"},"source":["## Initialize weights"]},{"cell_type":"markdown","metadata":{"id":"a25Zg2yyvhKL","colab_type":"text"},"source":["1. Randomly initialize the model's weights $W$ (we'll cover more effective initialization strategies later in this lesson)."]},{"cell_type":"code","metadata":{"id":"Y3udiI1Idr62","colab_type":"code","outputId":"2d6606df-d0fa-4a94-ca80-a17bce6eb0c8","executionInfo":{"status":"ok","timestamp":1583943920677,"user_tz":420,"elapsed":636,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Initialize first layer's weights\n","W1 = 0.01 * np.random.randn(INPUT_DIM, HIDDEN_DIM)\n","b1 = np.zeros((1, HIDDEN_DIM))\n","print (f\"W1: {W1.shape}\")\n","print (f\"b1: {b1.shape}\")"],"execution_count":48,"outputs":[{"output_type":"stream","text":["W1: (2, 100)\n","b1: (1, 100)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"oxF3FnErEKfr","colab_type":"text"},"source":["## Model"]},{"cell_type":"markdown","metadata":{"id":"CqllhIMgxGq7","colab_type":"text"},"source":["2. Feed inputs $X$ into the model to do the forward pass and receive the probabilities."]},{"cell_type":"markdown","metadata":{"id":"pH9-D8YE4pHH","colab_type":"text"},"source":["First we pass the inputs into the first layer.\n","  * $z_1 = XW_1$"]},{"cell_type":"code","metadata":{"id":"TAeYeoMtdr-t","colab_type":"code","outputId":"36deb286-7f14-4624-fe47-8adabafab6a5","executionInfo":{"status":"ok","timestamp":1583943923104,"user_tz":420,"elapsed":793,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# z1 = [NX2] · [2X100] + [1X100] = [NX100]\n","z1 = np.dot(X_train, W1) + b1\n","print (f\"z1: {z1.shape}\")"],"execution_count":49,"outputs":[{"output_type":"stream","text":["z1: (1083, 100)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"15yZEt903YOO","colab_type":"text"},"source":["Next we apply the non-linear activation function, ReLU ($max(0,z)$) in this case.\n","  * $a_1 = f(z_1)$"]},{"cell_type":"code","metadata":{"id":"yAFau-Zm3YVM","colab_type":"code","outputId":"2517437a-cb54-4687-cfa1-3472d8f44b9f","executionInfo":{"status":"ok","timestamp":1583943923865,"user_tz":420,"elapsed":357,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Apply activation function\n","a1 = np.maximum(0, z1) # ReLU\n","print (f\"a_1: {a1.shape}\")"],"execution_count":50,"outputs":[{"output_type":"stream","text":["a_1: (1083, 100)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gNHCPVa03YGB","colab_type":"text"},"source":["We pass the activations to the second layer to get our logits.\n","  * $z_2 = a_1W_2$"]},{"cell_type":"code","metadata":{"id":"kyJS1y2o3YJb","colab_type":"code","outputId":"97681475-2e36-4bb5-9ed1-82b237d1d59d","executionInfo":{"status":"ok","timestamp":1583943925359,"user_tz":420,"elapsed":463,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Initialize second layer's weights\n","W2 = 0.01 * np.random.randn(HIDDEN_DIM, NUM_CLASSES)\n","b2 = np.zeros((1, NUM_CLASSES))\n","print (f\"W2: {W2.shape}\")\n","print (f\"b2: {b2.shape}\")"],"execution_count":51,"outputs":[{"output_type":"stream","text":["W2: (100, 3)\n","b2: (1, 3)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"k_sCtbVh5DfI","colab_type":"code","outputId":"c29260af-3740-4aaa-d4db-a19bffc4c0be","executionInfo":{"status":"ok","timestamp":1583943926003,"user_tz":420,"elapsed":414,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# z2 = logits = [NX100] · [100X3] + [1X3] = [NX3]\n","logits = np.dot(a1, W2) + b2\n","print (f\"logits: {logits.shape}\")\n","print (f\"sample: {logits[0]}\")"],"execution_count":52,"outputs":[{"output_type":"stream","text":["logits: (1083, 3)\n","sample: [ 0.0004134   0.0002782  -0.00118021]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"6r_NhZuP3X5g","colab_type":"text"},"source":["We'll apply the softmax function to normalize the logits and btain class probabilities.\n","  * $\\hat{y} = softmax(z_2)$"]},{"cell_type":"code","metadata":{"id":"wI3jpHGJ3X_i","colab_type":"code","outputId":"5fba18ee-93cc-4848-ae4a-e4513e5211cf","executionInfo":{"status":"ok","timestamp":1583943927415,"user_tz":420,"elapsed":495,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Normalization via softmax to obtain class probabilities\n","exp_logits = np.exp(logits)\n","y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","print (f\"y_hat: {y_hat.shape}\")\n","print (f\"sample: {y_hat[0]}\")"],"execution_count":53,"outputs":[{"output_type":"stream","text":["y_hat: (1083, 3)\n","sample: [0.33352539 0.3334803  0.33299431]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"AhbLV8h7EPUT","colab_type":"text"},"source":["## Loss"]},{"cell_type":"markdown","metadata":{"id":"_VzjdcZG2h_h","colab_type":"text"},"source":["3. Compare the predictions $\\hat{y}$ (ex.  [0.3, 0.3, 0.4]]) with the actual target values $y$ (ex. class 2 would look like [0, 0, 1]) with the objective (cost) function to determine loss $J$. A common objective function for classification tasks is cross-entropy loss. \n","  * $J(\\theta) = - \\sum_i ln(\\hat{y_i}) = - \\sum_i ln (\\frac{e^{X_iW_y}}{\\sum_j e^{X_iW}}) $"]},{"cell_type":"code","metadata":{"id":"0llchiDhxGGI","colab_type":"code","colab":{}},"source":["# Loss\n","correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n","loss = np.sum(correct_class_logprobs) / len(y_train)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OsLXL8rxEYE6","colab_type":"text"},"source":["## Gradients"]},{"cell_type":"markdown","metadata":{"id":"6y4ZWFp32jd2","colab_type":"text"},"source":["4. Calculate the gradient of loss $J(\\theta)$ w.r.t to the model weights. \n","\n","The gradient of the loss w.r.t to W2 is the same as the gradients from logistic regression since $\\hat{y} = softmax(z_2)$.\n"," * $\\frac{\\partial{J}}{\\partial{W_{2j}}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2j}}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2j}}} = - \\frac{1}{\\frac{e^{W_{2y}a_1}}{\\sum_j e^{a_1W}}}\\frac{\\sum_j e^{a_1W}e^{a_1W_{2y}}0 - e^{a_1W_{2y}}e^{a_1W_{2j}}a_1}{(\\sum_j e^{a_1W})^2} = \\frac{a_1e^{a_1W_{2j}}}{\\sum_j e^{a_1W}} = a_1\\hat{y}$\n","  * $\\frac{\\partial{J}}{\\partial{W_{2y}}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2y}}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2y}}} = - \\frac{1}{\\frac{e^{W_{2y}a_1}}{\\sum_j e^{a_1W}}}\\frac{\\sum_j e^{a_1W}e^{a_1W_{2y}}a_1 - e^{a_1W_{2y}}e^{a_1W_{2y}}a_1}{(\\sum_j e^{a_1W})^2} = \\frac{1}{\\hat{y}}(a_1\\hat{y} - a_1\\hat{y}^2) = a_1(\\hat{y}-1)$\n","\n","  The gradient of the loss w.r.t W1 is a bit trickier since we have to backpropagate through two sets of weights.\n","  * $ \\frac{\\partial{J}}{\\partial{W_1}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}} \\frac{\\partial{\\hat{y}}}{\\partial{a_1}}  \\frac{\\partial{a_1}}{\\partial{z_1}}  \\frac{\\partial{z_1}}{\\partial{W_1}}  = W_2(\\partial{scores})(\\partial{ReLU})X $"]},{"cell_type":"code","metadata":{"id":"9D2ujwNOxGQ1","colab_type":"code","colab":{}},"source":["# dJ/dW2\n","dscores = y_hat\n","dscores[range(len(y_hat)), y_train] -= 1\n","dscores /= len(y_train)\n","dW2 = np.dot(a1.T, dscores)\n","db2 = np.sum(dscores, axis=0, keepdims=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gEpKpAgn78h4","colab_type":"code","colab":{}},"source":["# dJ/dW1\n","dhidden = np.dot(dscores, W2.T)\n","dhidden[a1 <= 0] = 0 # ReLu backprop\n","dW1 = np.dot(X_train.T, dhidden)\n","db1 = np.sum(dhidden, axis=0, keepdims=True)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"tKlRXHmoEafb","colab_type":"text"},"source":["## Update weights"]},{"cell_type":"markdown","metadata":{"id":"z1j-lgGq2lb_","colab_type":"text"},"source":["5. Update the weights $W$ using a small learning rate $\\alpha$. The updates will penalize the probabiltiy for the incorrect classes (j) and encourage a higher probability for the correct class (y).\n","  * $W_i = W_i - \\alpha\\frac{\\partial{J}}{\\partial{W_i}}$"]},{"cell_type":"code","metadata":{"id":"6kZoWyx-xGW8","colab_type":"code","colab":{}},"source":["# Update weights\n","W1 += -LEARNING_RATE * dW1\n","b1 += -LEARNING_RATE * db1\n","W2 += -LEARNING_RATE * dW2\n","b2 += -LEARNING_RATE * db2"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7K8S9rf1EdHy","colab_type":"text"},"source":["## Training"]},{"cell_type":"markdown","metadata":{"id":"6lho6k572mge","colab_type":"text"},"source":["6. Repeat steps 2 - 4 until model performs well."]},{"cell_type":"code","metadata":{"id":"BFH0tHlZaoaS","colab_type":"code","colab":{}},"source":["# Convert tensors to NumPy arrays\n","X_train = X_train.numpy()\n","y_train = y_train.numpy()\n","X_val = X_val.numpy()\n","y_val = y_val.numpy()\n","X_test = X_test.numpy()\n","y_test = y_test.numpy()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ScHz7h6OxGU_","colab_type":"code","outputId":"da7ca5fa-a7cf-4109-8a49-46c04617e9a6","executionInfo":{"status":"ok","timestamp":1583944014525,"user_tz":420,"elapsed":4903,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# Initialize random weights\n","W1 = 0.01 * np.random.randn(INPUT_DIM, HIDDEN_DIM)\n","b1 = np.zeros((1, HIDDEN_DIM))\n","W2 = 0.01 * np.random.randn(HIDDEN_DIM, NUM_CLASSES)\n","b2 = np.zeros((1, NUM_CLASSES))\n","\n","# Training loop\n","for epoch_num in range(1000):\n","\n","    # First layer forward pass [NX2] · [2X100] = [NX100]\n","    z1 = np.dot(X_train, W1) + b1\n","\n","    # Apply activation function\n","    a1 = np.maximum(0, z1) # ReLU\n","\n","    # z2 = logits = [NX100] · [100X3] = [NX3]\n","    logits = np.dot(a1, W2) + b2\n","    \n","    # Normalization via softmax to obtain class probabilities\n","    exp_logits = np.exp(logits)\n","    y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","\n","    # Loss\n","    correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n","    loss = np.sum(correct_class_logprobs) / len(y_train)\n","\n","    # show progress\n","    if epoch_num%100 == 0:\n","        # Accuracy\n","        y_pred = np.argmax(logits, axis=1)\n","        accuracy =  np.mean(np.equal(y_train, y_pred))\n","        print (f\"Epoch: {epoch_num}, loss: {loss:.3f}, accuracy: {accuracy:.3f}\")\n","\n","    # dJ/dW2\n","    dscores = y_hat\n","    dscores[range(len(y_hat)), y_train] -= 1\n","    dscores /= len(y_train)\n","    dW2 = np.dot(a1.T, dscores)\n","    db2 = np.sum(dscores, axis=0, keepdims=True)\n","\n","    # dJ/dW1\n","    dhidden = np.dot(dscores, W2.T)\n","    dhidden[a1 <= 0] = 0 # ReLu backprop\n","    dW1 = np.dot(X_train.T, dhidden)\n","    db1 = np.sum(dhidden, axis=0, keepdims=True)\n","\n","    # Update weights\n","    W1 += -1e0 * dW1\n","    b1 += -1e0 * db1\n","    W2 += -1e0 * dW2\n","    b2 += -1e0 * db2"],"execution_count":62,"outputs":[{"output_type":"stream","text":["Epoch: 0, loss: 1.099, accuracy: 0.519\n","Epoch: 100, loss: 0.477, accuracy: 0.761\n","Epoch: 200, loss: 0.204, accuracy: 0.924\n","Epoch: 300, loss: 0.120, accuracy: 0.951\n","Epoch: 400, loss: 0.084, accuracy: 0.981\n","Epoch: 500, loss: 0.064, accuracy: 0.990\n","Epoch: 600, loss: 0.052, accuracy: 0.995\n","Epoch: 700, loss: 0.045, accuracy: 0.998\n","Epoch: 800, loss: 0.040, accuracy: 0.998\n","Epoch: 900, loss: 0.036, accuracy: 0.998\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"2hDGiO6Ng8CI","colab_type":"code","colab":{}},"source":["class MLPFromScratch():\n","    def predict(self, x):\n","        z1 = np.dot(x, W1) + b1\n","        a1 = np.maximum(0, z1)\n","        logits = np.dot(a1, W2) + b2\n","        exp_logits = np.exp(logits)\n","        y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","        return y_hat"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Dv5Y3-1ibuPM","colab_type":"code","colab":{}},"source":["# Evaluation\n","model = MLPFromScratch()\n","logits_train = model.predict(X_train)\n","pred_train = np.argmax(logits_train, axis=1)\n","logits_test = model.predict(X_test)\n","pred_test = np.argmax(logits_test, axis=1)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XfmYidNSgkcv","colab_type":"code","outputId":"7850d4f1-699d-42fa-fef8-b699db57ffe2","executionInfo":{"status":"ok","timestamp":1583944016488,"user_tz":420,"elapsed":934,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Training and test accuracy\n","train_acc =  np.mean(np.equal(y_train, pred_train))\n","test_acc = np.mean(np.equal(y_test, pred_test))\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":65,"outputs":[{"output_type":"stream","text":["train acc: 1.00, test acc: 1.00\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gesG_0FIdRom","colab_type":"code","colab":{}},"source":["def plot_multiclass_decision_boundary_numpy(model, X, y, savefig_fp=None):\n","    \"\"\"Plot the multiclass decision boundary for a model that accepts 2D inputs.\n","\n","    Arguments:\n","        model {function} -- trained model with function model.predict(x_in).\n","        X {numpy.ndarray} -- 2D inputs with shape (N, 2).\n","        y {numpy.ndarray} -- 1D outputs with shape (N,).\n","    \"\"\"\n","    # Axis boundaries\n","    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n","    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n","    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101),\n","                         np.linspace(y_min, y_max, 101))\n","\n","    # Create predictions\n","    x_in = np.c_[xx.ravel(), yy.ravel()]\n","    y_pred = model.predict(x_in)\n","    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n","\n","    # Plot decision boundary\n","    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n","    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n","    plt.xlim(xx.min(), xx.max())\n","    plt.ylim(yy.min(), yy.max())\n","\n","    # Plot\n","    if savefig_fp:\n","        plt.savefig(savefig_fp, format='png')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"I5pDl4hdgkaE","colab_type":"code","outputId":"3f6b6ba9-72a0-4326-9c1e-7a85adf7f08f","executionInfo":{"status":"ok","timestamp":1583944018519,"user_tz":420,"elapsed":967,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":336}},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary_numpy(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary_numpy(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":67,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsEAAAE/CAYAAACnwR6AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYKWUUkopVXc0CFZKKaWUUnXn/wMaDo3tu1yXFgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"kV2rzKCLdSEu","colab_type":"text"},"source":["Credit for the plotting functions and the intuition behind all this is due to [CS231n](http://cs231n.github.io/neural-networks-case-study/), one of the best courses for machine learning. Now let's implement the MLP with PyTorch."]},{"cell_type":"markdown","metadata":{"id":"br_O_NUCjkwm","colab_type":"text"},"source":["# MLP"]},{"cell_type":"markdown","metadata":{"id":"kNwK9qm6FpVm","colab_type":"text"},"source":["## Model"]},{"cell_type":"code","metadata":{"id":"YtZvQVVpvl5p","colab_type":"code","colab":{}},"source":["class MLP(nn.Module):\n","    def __init__(self, input_dim, hidden_dim, num_classes):\n","        super(MLP, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, hidden_dim)\n","        self.fc2 = nn.Linear(hidden_dim, num_classes)\n","        \n","    def forward(self, x_in, apply_softmax=False):\n","        z = F.relu(self.fc1(x_in)) # ReLU activaton function added!\n","        y_pred = self.fc2(z)\n","        if apply_softmax:\n","            y_pred = F.softmax(y_pred, dim=1) \n","        return y_pred"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NwnvrAUGFrZW","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":340},"outputId":"1a1a75a6-65e1-46e1-99f7-8bacc462ca6a","executionInfo":{"status":"ok","timestamp":1583944048399,"user_tz":420,"elapsed":399,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}}},"source":["# Initialize model\n","model = MLP(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, num_classes=NUM_CLASSES)\n","print (model.named_parameters)\n","summary(model, input_size=(INPUT_DIM,))"],"execution_count":70,"outputs":[{"output_type":"stream","text":["<bound method Module.named_parameters of MLP(\n","  (fc1): Linear(in_features=2, out_features=100, bias=True)\n","  (fc2): Linear(in_features=100, out_features=3, bias=True)\n",")>\n","----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Linear-1                  [-1, 100]             300\n","            Linear-2                    [-1, 3]             303\n","================================================================\n","Total params: 603\n","Trainable params: 603\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.00\n","Forward/backward pass size (MB): 0.00\n","Params size (MB): 0.00\n","Estimated Total Size (MB): 0.00\n","----------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"o942ZstYFtHm","colab_type":"text"},"source":["## Training"]},{"cell_type":"code","metadata":{"id":"K5fIv-0sbpJ8","colab_type":"code","colab":{}},"source":["# Loss\n","weights = torch.Tensor([class_weights[key] for key in sorted(class_weights.keys())])\n","loss_fn = nn.CrossEntropyLoss(weight=weights)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"TN0Jh_drczUh","colab":{}},"source":["# Accuracy\n","def accuracy_fn(y_pred, y_true):\n","    n_correct = torch.eq(y_pred, y_true).sum().item()\n","    accuracy = (n_correct / len(y_pred)) * 100\n","    return accuracy"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"nmeU45XF0O-U","colab":{}},"source":["# Optimizer\n","optimizer = Adam(model.parameters(), lr=LEARNING_RATE) "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"SHKHbn4xFwjl","colab_type":"code","colab":{}},"source":["# Convert data to tensors\n","X_train = torch.Tensor(X_train)\n","y_train = torch.LongTensor(y_train)\n","X_val = torch.Tensor(X_val)\n","y_val = torch.LongTensor(y_val)\n","X_test = torch.Tensor(X_test)\n","y_test = torch.LongTensor(y_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"SJc5obfebwEp","colab_type":"code","outputId":"92ef2705-b9f3-404e-c365-63ac19aa1d40","executionInfo":{"status":"ok","timestamp":1583944065247,"user_tz":420,"elapsed":617,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# Training\n","for epoch in range(NUM_EPOCHS*10):\n","    # Forward pass\n","    y_pred = model(X_train)\n","\n","    # Loss\n","    loss = loss_fn(y_pred, y_train)\n","\n","    # Zero all gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","\n","    # Update weights\n","    optimizer.step()\n","\n","    if epoch%10==0: \n","        predictions = y_pred.max(dim=1)[1] # class\n","        accuracy = accuracy_fn(y_pred=predictions, y_true=y_train)\n","        print (f\"Epoch: {epoch} | loss: {loss:.2f}, accuracy: {accuracy:.1f}\")"],"execution_count":77,"outputs":[{"output_type":"stream","text":["Epoch: 0 | loss: 1.09, accuracy: 21.3\n","Epoch: 10 | loss: 0.66, accuracy: 58.1\n","Epoch: 20 | loss: 0.49, accuracy: 72.1\n","Epoch: 30 | loss: 0.37, accuracy: 89.2\n","Epoch: 40 | loss: 0.27, accuracy: 91.4\n","Epoch: 50 | loss: 0.21, accuracy: 93.5\n","Epoch: 60 | loss: 0.16, accuracy: 95.2\n","Epoch: 70 | loss: 0.14, accuracy: 95.8\n","Epoch: 80 | loss: 0.11, accuracy: 96.8\n","Epoch: 90 | loss: 0.10, accuracy: 97.9\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"10HGpWGTF4oR","colab_type":"text"},"source":["## Evaluation"]},{"cell_type":"code","metadata":{"id":"Y4tI2iZ1vl8e","colab_type":"code","outputId":"af9b2ff7-21ab-4527-b60f-cfe586a129a7","executionInfo":{"status":"ok","timestamp":1583944068431,"user_tz":420,"elapsed":367,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Predictions\n","pred_train = model(X_train, apply_softmax=True)\n","pred_test = model(X_test, apply_softmax=True)\n","print (f\"sample probability: {pred_test[0]}\")\n","pred_train = pred_train.max(dim=1)[1]\n","pred_test = pred_test.max(dim=1)[1]\n","print (f\"sample class: {pred_test[0]}\")"],"execution_count":78,"outputs":[{"output_type":"stream","text":["sample probability: tensor([6.8674e-03, 7.5599e-04, 9.9238e-01], grad_fn=<SelectBackward>)\n","sample class: 2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Ieu55J1Gc7IK","colab_type":"code","outputId":"efe78ef6-8031-4cd6-987a-64807dbf52b3","executionInfo":{"status":"ok","timestamp":1583944069194,"user_tz":420,"elapsed":282,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Accuracy\n","train_acc = accuracy_score(y_train, pred_train)\n","test_acc = accuracy_score(y_test, pred_test)\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":79,"outputs":[{"output_type":"stream","text":["train acc: 0.98, test acc: 0.99\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"CgM4qu8I62BP","colab_type":"code","outputId":"73273a92-b5d7-4a69-b359-97f509711d57","executionInfo":{"status":"ok","timestamp":1583944070468,"user_tz":420,"elapsed":1000,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":476}},"source":["# Metrics\n","plot_confusion_matrix(y_test, pred_test, classes=classes)\n","print (classification_report(y_test, pred_test))"],"execution_count":80,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAATcAAAEhCAYAAAAaree0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkP4fZwn4Ifd55LUAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.97      0.99      0.98        75\n","           1       1.00      0.97      0.99        75\n","           2       0.99      1.00      0.99        75\n","\n","    accuracy                           0.99       225\n","   macro avg       0.99      0.99      0.99       225\n","weighted avg       0.99      0.99      0.99       225\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Xj7cwzZ4NoVI","colab_type":"code","outputId":"ed63c22e-0a4f-48eb-bccb-c3a0bfd5d189","executionInfo":{"status":"ok","timestamp":1583944072549,"user_tz":420,"elapsed":935,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":336}},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":81,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsEAAAE/CAYAAACnwR6AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"6Cv3ev9Uo1Ii","colab_type":"text"},"source":["## Inference"]},{"cell_type":"code","metadata":{"id":"h25pUbR8o9eF","colab_type":"code","outputId":"373d8ab6-b420-4453-aa20-1106c0335538","executionInfo":{"status":"ok","timestamp":1583944080811,"user_tz":420,"elapsed":591,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":80}},"source":["# Inputs for inference\n","X_infer = pd.DataFrame([{'X1': 0.1, 'X2': 0.1}])\n","X_infer.head()"],"execution_count":82,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X1</th>\n","      <th>X2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.1</td>\n","      <td>0.1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    X1   X2\n","0  0.1  0.1"]},"metadata":{"tags":[]},"execution_count":82}]},{"cell_type":"code","metadata":{"id":"8G5lIYReo9l_","colab_type":"code","outputId":"60c0c5a6-1292-4095-aa47-7eedc6dd39ca","executionInfo":{"status":"ok","timestamp":1583944081841,"user_tz":420,"elapsed":869,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Standardize\n","X_infer = X_scaler.transform(X_infer)\n","print (X_infer)"],"execution_count":83,"outputs":[{"output_type":"stream","text":["[[0.30945845 0.30761858]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Uow5g2-1o9kF","colab_type":"code","outputId":"75cdd915-f011-4a6e-d7a9-9e1f7ad7786a","executionInfo":{"status":"ok","timestamp":1583944083068,"user_tz":420,"elapsed":510,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Predict\n","y_infer = model(torch.Tensor(X_infer), apply_softmax=True)\n","prob, _class = y_infer.max(dim=1)\n","print (f\"The probability of class {classes[_class.detach().numpy()[0]]} is {prob.detach().numpy()[0]*100.0:.0f}%\")"],"execution_count":84,"outputs":[{"output_type":"stream","text":["The probability of class c1 is 96%\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gWv-RU46k2UL","colab_type":"text"},"source":["# Initializing weights"]},{"cell_type":"markdown","metadata":{"id":"1hDPBE0sk2mJ","colab_type":"text"},"source":["So far we have been initializing weights with small random values and this isn't optimal for convergence during training. The objective is to have weights that are able to produce outputs that follow a similar distribution across all neurons. We can do this by enforcing weights to have unit variance prior the affine and non-linear operations."]},{"cell_type":"markdown","metadata":{"id":"YxCm7FyRFRWK","colab_type":"text"},"source":["**NOTE**: A popular method is to apply [xavier initialization](http://andyljones.tumblr.com/post/110998971763/an-explanation-of-xavier-initialization), which essentially initializes the weights to allow the signal from the data to reach deep into the network. You may be wondering why we don't do this for every forward pass and that's a great question. We'll look at more advanced strategies that help with optimization like batch/layer normalization, etc. in future lessons. Meanwhile you can check out other initializers [here](https://pytorch.org/docs/stable/nn.init.html)."]},{"cell_type":"code","metadata":{"id":"Dj7apAxufge7","colab_type":"code","colab":{}},"source":["from torch.nn import init"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"U5lUR__TKvqA","colab_type":"code","colab":{}},"source":["class MLP(nn.Module):\n","    def __init__(self, input_dim, hidden_dim, num_classes):\n","        super(MLP, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, hidden_dim)\n","        self.fc2 = nn.Linear(hidden_dim, num_classes)\n","\n","    def init_weights(self):\n","        init.xavier_normal(self.fc1.weight, gain=init.calculate_gain('relu')) \n","        \n","    def forward(self, x_in, apply_softmax=False):\n","        z = F.relu(self.fc1(x_in)) # ReLU activaton function added!\n","        y_pred = self.fc2(z)\n","        if apply_softmax:\n","            y_pred = F.softmax(y_pred, dim=1) \n","        return y_pred"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FAyjh3bieLjn","colab_type":"text"},"source":["# Dropout"]},{"cell_type":"markdown","metadata":{"id":"z6kLwcBveLy5","colab_type":"text"},"source":["A great technique to have our models generalize (perform well on test data) is to increase the size of your data but this isn't always an option. Fortuntely, there are methods like regularization and dropout that can help create a more robust model. \n","\n","Dropout is a technique (used only during training) that allows us to zero the outputs of neurons. We do this for `dropout_p`% of the total neurons in each layer and it changes every batch. Dropout prevents units from co-adapting too much to the data and acts as a sampling strategy since we drop a different set of neurons each time.\n","\n","<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/madewithml/images/master/basics/09_Multilayer_Perceptron/dropout.png\" width=\"350\">\n","</div>\n","\n","* [Dropout: A Simple Way to Prevent Neural Networks from\n","Overfitting](http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf)"]},{"cell_type":"code","metadata":{"id":"g1E2bDj2HKpb","colab_type":"code","colab":{}},"source":["DROPOUT_P = 0.1 # % of the neurons that are dropped each pass"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DS26SgeFgvG1","colab_type":"code","colab":{}},"source":["class MLP(nn.Module):\n","    def __init__(self, input_dim, hidden_dim, dropout_p, num_classes):\n","        super(MLP, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, hidden_dim)\n","        self.dropout = nn.Dropout(dropout_p) # dropout\n","        self.fc2 = nn.Linear(hidden_dim, num_classes)\n","\n","    def init_weights(self):\n","        init.xavier_normal(self.fc1.weight, gain=init.calculate_gain('relu')) \n","        \n","    def forward(self, x_in, apply_softmax=False):\n","        z = F.relu(self.fc1(x_in)) \n","        z = self.dropout(z) # dropout\n","        y_pred = self.fc2(z)\n","        if apply_softmax:\n","            y_pred = F.softmax(y_pred, dim=1) \n","        return y_pred"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"1he_g3eJgvOk","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":374},"outputId":"fba356b9-8a09-42e5-9123-e2c34094a52e","executionInfo":{"status":"ok","timestamp":1583944124985,"user_tz":420,"elapsed":335,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}}},"source":["# Initialize model\n","model = MLP(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, \n","            dropout_p=DROPOUT_P, num_classes=NUM_CLASSES)\n","print (model.named_parameters)\n","summary(model, input_size=(INPUT_DIM,))"],"execution_count":89,"outputs":[{"output_type":"stream","text":["<bound method Module.named_parameters of MLP(\n","  (fc1): Linear(in_features=2, out_features=100, bias=True)\n","  (dropout): Dropout(p=0.1, inplace=False)\n","  (fc2): Linear(in_features=100, out_features=3, bias=True)\n",")>\n","----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Linear-1                  [-1, 100]             300\n","           Dropout-2                  [-1, 100]               0\n","            Linear-3                    [-1, 3]             303\n","================================================================\n","Total params: 603\n","Trainable params: 603\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.00\n","Forward/backward pass size (MB): 0.00\n","Params size (MB): 0.00\n","Estimated Total Size (MB): 0.00\n","----------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"9ijvHwcZg8mO","colab_type":"text"},"source":["# Overfitting"]},{"cell_type":"markdown","metadata":{"id":"FIhvdD_zg8os","colab_type":"text"},"source":["Though neural networks are great at capturing non-linear relationships they are highly susceptible to overfitting to the training data and failing to generalize on test data. Just take a look at the example below where we generate completely random data and are able to fit a model with [$2*N*C + D$](https://arxiv.org/abs/1611.03530) hidden units. The training performance is good (~70%) but the overfitting leads to very poor test performance. We'll be covering strategies to tackle overfitting in future lessons."]},{"cell_type":"code","metadata":{"id":"uRdM16NhazJP","colab_type":"code","colab":{}},"source":["NUM_EPOCHS = 500\n","NUM_SAMPLES_PER_CLASS = 50\n","LEARNING_RATE = 1e-1\n","HIDDEN_DIM = 2 * NUM_SAMPLES_PER_CLASS * NUM_CLASSES + INPUT_DIM # 2*N*C + D"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"qf00Biq6g8ty","colab_type":"code","outputId":"3fc8d53c-e51c-469d-b1b9-a8cfb535c5c2","executionInfo":{"status":"ok","timestamp":1583944323259,"user_tz":420,"elapsed":737,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Generate random data\n","X = np.random.rand(NUM_SAMPLES_PER_CLASS * NUM_CLASSES, INPUT_DIM)\n","y = np.array([[i]*NUM_SAMPLES_PER_CLASS for i in range(NUM_CLASSES)]).reshape(-1)\n","print (\"X: \", format(np.shape(X)))\n","print (\"y: \", format(np.shape(y)))"],"execution_count":140,"outputs":[{"output_type":"stream","text":["X:  (150, 2)\n","y:  (150,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-bA9vK9SWkjh","colab_type":"code","outputId":"cb1accf4-e1ae-43ec-9c16-1ef4fa4cda0e","executionInfo":{"status":"ok","timestamp":1583944323636,"user_tz":420,"elapsed":769,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":119}},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X, y, val_size=VAL_SIZE, test_size=TEST_SIZE, shuffle=SHUFFLE)\n","print (\"X_train:\", X_train.shape)\n","print (\"y_train:\", y_train.shape)\n","print (\"X_val:\", X_val.shape)\n","print (\"y_val:\", y_val.shape)\n","print (\"X_test:\", X_test.shape)\n","print (\"y_test:\", y_test.shape)"],"execution_count":141,"outputs":[{"output_type":"stream","text":["X_train: (107, 2)\n","y_train: (107,)\n","X_val: (20, 2)\n","y_val: (20,)\n","X_test: (23, 2)\n","y_test: (23,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"lYwc9mk1f5bm","colab_type":"code","colab":{}},"source":["# Standardize the inputs (mean=0, std=1) using training data\n","X_scaler = StandardScaler().fit(X_train)\n","X_train = X_scaler.transform(X_train)\n","X_val = X_scaler.transform(X_val)\n","X_test = X_scaler.transform(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ogTctxN3gt8M","colab_type":"code","colab":{}},"source":["# Convert data to tensors\n","X_train = torch.Tensor(X_train)\n","y_train = torch.LongTensor(y_train)\n","X_val = torch.Tensor(X_val)\n","y_val = torch.LongTensor(y_val)\n","X_test = torch.Tensor(X_test)\n","y_test = torch.LongTensor(y_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DDUW0xVMh8v8","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":374},"outputId":"4e651615-7e56-49f3-e2d1-080a114ccecc","executionInfo":{"status":"ok","timestamp":1583944324205,"user_tz":420,"elapsed":232,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}}},"source":["# Initialize model\n","model = MLP(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, \n","            dropout_p=DROPOUT_P, num_classes=NUM_CLASSES)\n","print (model.named_parameters)\n","summary(model, input_size=(INPUT_DIM,))"],"execution_count":144,"outputs":[{"output_type":"stream","text":["<bound method Module.named_parameters of MLP(\n","  (fc1): Linear(in_features=2, out_features=302, bias=True)\n","  (dropout): Dropout(p=0.1, inplace=False)\n","  (fc2): Linear(in_features=302, out_features=3, bias=True)\n",")>\n","----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Linear-1                  [-1, 302]             906\n","           Dropout-2                  [-1, 302]               0\n","            Linear-3                    [-1, 3]             909\n","================================================================\n","Total params: 1,815\n","Trainable params: 1,815\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.00\n","Forward/backward pass size (MB): 0.00\n","Params size (MB): 0.01\n","Estimated Total Size (MB): 0.01\n","----------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"6bIiiaOmhFiN","colab_type":"code","colab":{}},"source":["# Optimizer\n","optimizer = Adam(model.parameters(), lr=LEARNING_RATE) "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zgayj4E1WksH","colab_type":"code","outputId":"9a238eb8-5540-4b3a-936f-7f221cde1c51","executionInfo":{"status":"ok","timestamp":1583944326782,"user_tz":420,"elapsed":1452,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":442}},"source":["# Training\n","for epoch in range(NUM_EPOCHS):\n","    # Forward pass\n","    y_pred = model(X_train)\n","\n","    # Loss\n","    loss = loss_fn(y_pred, y_train)\n","\n","    # Zero all gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","\n","    # Update weights\n","    optimizer.step()\n","\n","    if epoch%20==0: \n","        predictions = y_pred.max(dim=1)[1] # class\n","        accuracy = accuracy_fn(y_pred=predictions, y_true=y_train)\n","        print (f\"Epoch: {epoch} | loss: {loss:.2f}, accuracy: {accuracy:.1f}\")"],"execution_count":146,"outputs":[{"output_type":"stream","text":["Epoch: 0 | loss: 1.12, accuracy: 34.6\n","Epoch: 20 | loss: 1.07, accuracy: 38.3\n","Epoch: 40 | loss: 1.01, accuracy: 48.6\n","Epoch: 60 | loss: 0.98, accuracy: 53.3\n","Epoch: 80 | loss: 0.97, accuracy: 53.3\n","Epoch: 100 | loss: 0.97, accuracy: 52.3\n","Epoch: 120 | loss: 0.91, accuracy: 58.9\n","Epoch: 140 | loss: 0.93, accuracy: 56.1\n","Epoch: 160 | loss: 0.94, accuracy: 53.3\n","Epoch: 180 | loss: 0.89, accuracy: 56.1\n","Epoch: 200 | loss: 0.93, accuracy: 57.0\n","Epoch: 220 | loss: 0.94, accuracy: 51.4\n","Epoch: 240 | loss: 0.90, accuracy: 57.0\n","Epoch: 260 | loss: 0.96, accuracy: 53.3\n","Epoch: 280 | loss: 0.94, accuracy: 55.1\n","Epoch: 300 | loss: 0.85, accuracy: 57.9\n","Epoch: 320 | loss: 0.87, accuracy: 59.8\n","Epoch: 340 | loss: 0.86, accuracy: 60.7\n","Epoch: 360 | loss: 0.85, accuracy: 60.7\n","Epoch: 380 | loss: 0.86, accuracy: 53.3\n","Epoch: 400 | loss: 0.86, accuracy: 61.7\n","Epoch: 420 | loss: 0.90, accuracy: 56.1\n","Epoch: 440 | loss: 0.89, accuracy: 53.3\n","Epoch: 460 | loss: 0.82, accuracy: 62.6\n","Epoch: 480 | loss: 0.86, accuracy: 58.9\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8QG4bPzshN8L","colab_type":"code","outputId":"634735eb-826a-4b42-8161-f97f22aba311","executionInfo":{"status":"ok","timestamp":1583944328132,"user_tz":420,"elapsed":507,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Predictions\n","pred_train = model(X_train, apply_softmax=True)\n","pred_test = model(X_test, apply_softmax=True)\n","print (f\"sample probability: {pred_test[0]}\")\n","pred_train = pred_train.max(dim=1)[1]\n","pred_test = pred_test.max(dim=1)[1]\n","print (f\"sample class: {pred_test[0]}\")"],"execution_count":147,"outputs":[{"output_type":"stream","text":["sample probability: tensor([0.0514, 0.1081, 0.8405], grad_fn=<SelectBackward>)\n","sample class: 2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bDoiOTgxdgLd","colab_type":"code","outputId":"bf3f01fd-166c-498a-a483-2891f05d0980","executionInfo":{"status":"ok","timestamp":1583944328756,"user_tz":420,"elapsed":505,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Accuracy\n","train_acc = accuracy_score(y_train, pred_train)\n","test_acc = accuracy_score(y_test, pred_test)\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":148,"outputs":[{"output_type":"stream","text":["train acc: 0.63, test acc: 0.26\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"_LU9Wzt0ZxwI","colab_type":"code","outputId":"f5c5a6be-0e5a-4f3b-a5cb-c1cfc64d8d6f","executionInfo":{"status":"ok","timestamp":1583944329426,"user_tz":420,"elapsed":839,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":476}},"source":["# Classification report\n","plot_confusion_matrix(y_true=y_test, y_pred=pred_test, classes=classes)\n","print (classification_report(y_test, pred_test))"],"execution_count":149,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAATEAAAEhCAYAAAAXs5fzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.00      0.00      0.00         8\n","           1       0.29      0.57      0.38         7\n","           2       0.40      0.25      0.31         8\n","\n","    accuracy                           0.26        23\n","   macro avg       0.23      0.27      0.23        23\n","weighted avg       0.23      0.26      0.22        23\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rpSoAEdGWku5","colab_type":"code","outputId":"a0529612-9a0a-4e84-9036-89098160fc99","executionInfo":{"status":"ok","timestamp":1583944330908,"user_tz":420,"elapsed":1206,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":336}},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":150,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAs8AAAE/CAYAAAC5CC4zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"yppKnFIL6Vv7","colab_type":"text"},"source":["It's important that we experiment, starting with simple models that underfit (high bias) and improve it towards a good fit. Starting with simple models (linear/logistic regression) let's us catch errors without the added complexity of more sophisticated models (neural networks). "]},{"cell_type":"markdown","metadata":{"id":"WC0m-lMnE4Jp","colab_type":"text"},"source":["<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/madewithml/images/master/basics/09_Multilayer_Perceptron/fit.png\" width=\"700\">\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"yTkd_cN_io5D","colab_type":"text"},"source":["---\n","Share and discover ML projects at <a href=\"https://madewithml.com/\">Made With ML</a>.\n","\n","<div align=\"left\">\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://github.com/madewithml/basics\"><img src=\"https://img.shields.io/github/stars/madewithml/basics.svg?style=social&label=Star\"></a>&nbsp;\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://www.linkedin.com/company/madewithml\"><img src=\"https://img.shields.io/badge/style--5eba00.svg?label=LinkedIn&logo=linkedin&style=social\"></a>&nbsp;\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://twitter.com/madewithml\"><img src=\"https://img.shields.io/twitter/follow/madewithml.svg?label=Follow&style=social\"></a>\n","</div>\n","             "]}]}